Cars Analysis and Modeling
Group 1
5/05/2021
Abstract: Description of the project and the findings
The dataset in question is of used cars in a popular catalog from the country of Belarus. This dataset will be cleaned and analyzed, to answer several questions about the effect that certain variables have on the price point at which cars are sold at. Ultimately, Multiple Linear Regression Models, SVR, Decision Tree Regression, Random Forest Regression, and KNN will all be used to predict the market price of a used car in Belarus given the attributes found in the dataset. The accuracy of these models were analyzed and performance tested for real-world application with the help of a training and testing dataset. Afterwords, the models were compared to find the most effective model for predicting the price of a used car in Belarus. STATE OUR FINDINGS
Introduction: Overview of the problems/goals and how they will be solved
We went through many datasets to find the one we were all interested in. We chose this dataset because it had a significant amount of samples, and there was a mixture of continuous and categorical variables. Since cars are such a big investment we all wondered what affected the price on cars. This is what sparked our initial questions and goals. Solving our proposed questions and goal we hope to gain insights in what affects car prices which would leave us with more knowledge we can use later in life. To solve our questions and answer our goal we are going to use R to make graphs to answer our questions. We will make sure to use different statistical analyses to make sure our graphs are confirmed to be accurate.
Initial Questions
The main goal is to create a predictive model based on insights gained from analyzing the impact of variables on the selling price of a vehicle. The goal was chosen for its applicability and general interest of which attributes impact price of a used car. In addition, there will be several questions that will be answered concerning the dataset. These questions aid in solving the main goal and are important insights to be gleaned from the dataset. The questions that will be answered about the dataset are as follows:
- What impact does a region have on price?
- What is the distribution of manufacturers and whether manufacturers have a significant impact on the asking price of a vehicle?
- What is the relationship between odometer and price?
- Does the number of photos a vehicle has impact the selling price?
- Does the number of times a vehicle has been upped in the catalog to raise its position impact the selling price?
- Relationship between Engine Type and Body Type? What is the impact of Engine Type and Body Type on the selling price?
- What is the most popular model and whether we can conclude that the popularity of a model has a direct impact on the price of a vehicle?
- What is the average age of each vehicle manufacturer and whether the manufacturer changes how the production year impacts the selling price?
Data Analysis and Modeling:
Visualizations used to analyze data
In an attempt to gain a robust knowledge of our dataset several visualizations were used. Visualization of our data was done in two sections. Initially each attribute was graphed in order to gain a general understanding of how the samples are distributed for each attribute. This was done with the help of Bar Graphs, Histograms, Boxplots, the count function, and pie graphs.
# 1)What is the distribution of manufacturers?
ggplot(cars_edited, aes(y = manufacturer_name)) + geom_bar(aes(fill = manufacturer_name)) + geom_text(stat='count', aes(label=..count..), hjust=1)
# We can see a large difference in the amount cars for each manufacturers. Volkswagen, Opel, BMW, Audio, AvtoVAZ, Ford, Renault, and Mercedes-Benz are the majot manufacturers.# 2) A table to show unique car model names and quantity
View(cars_edited %>% count(model_name))
# 3) Plotting the number of cars with automatic or mechanical transmissions
transmissionGrouped <- group_by(cars_edited, transmission)
transmissionCounted <- count(transmissionGrouped)
percentTransmission <- paste0(round(100*transmissionCounted$n/sum(transmissionCounted$n), 2), "%")
pie(transmissionCounted$n, labels = percentTransmission, main = "Transmission Distribution", col = rainbow(nrow(transmissionCounted)))
legend("right", c("Automatic", "Mechanical"), cex = 0.8,
fill = rainbow(length(transmissionCounted)))
# Mechanical is significantly more common than Automatic. This will definitely be an attribute to consider in our final model# 4) Plotting cars by color and quantity
ggplot(cars_edited, aes(x = color)) + geom_bar(stat = "count", aes(fill = color)) + geom_text(stat = "count", aes(label = after_stat(count)), vjust = -1)
# There is a lot of diversity in the colors and once again although there are categories with more values there is still a decent amount of variation
# 5) Histogram Odometer Value: Graph to see how the data is skewed
ggplot(cars_edited, aes(odometer_value)) + geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# The data is left-skewed with what appears to be outliers as around 1,000,000 miles
# 6) Histogram Year produced: Graph to see how the data is skewed
ggplot(cars_edited) + geom_histogram(aes(year_produced))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# The graph seems to almost be normally distributed minus what appears to be some outliers on the the older end of the years# 7) Graph to show what fuel distribution
ggplot(cars_edited, aes(x = engine_fuel)) + geom_bar(stat = "count", aes(fill = engine_fuel)) + geom_text(stat = "count", aes(label = after_stat(count)), vjust = -1)
# There are only 2 major engine fuels(gasoline and diesel)# 8) Pie graph to show engine type Distribution (Electric, Diesel, Gasoline)
TypeGrouped <- group_by(cars_edited, engine_type)
TypeCounted <- count(TypeGrouped)
percentType <- paste0(round(100*TypeCounted$n/sum(TypeCounted$n), 2), "%")
pie(TypeCounted$n, labels = percentType, main = "Engine Type Distribution", col = rainbow(nrow(TypeCounted)))
legend("right", c("diesel", "electric", "gasoline"), cex = 0.8,
fill = rainbow(nrow(TypeCounted)))
# Not surprisingly gasoline and diesel are the 2 most common Engine Type considering the fuel distribution
# 9) Table for Engine capacity
View(cars_edited %>% count(engine_capacity))
# Engine Capacity seems to be left-skewed which may indicate outliers
# 10) Bar graph Body type: count how many cars have the same body type
ggplot(cars_edited, aes(x = body_type), stat = "count") + geom_bar() + geom_text(stat = "count", aes(label = after_stat(count)), vjust = -1)
# There is some diversity in body type and the diversity in categories may lend itself to useful data for a future model
# 11) Graph Drivetrain distribution:
drivetrainGrouped <- group_by(cars_edited, drivetrain)
drivetrainCounted <- count(drivetrainGrouped)
percentdrivetrain <- paste0(round(100*drivetrainCounted$n/sum(drivetrainCounted$n), 2), "%")
pie(drivetrainCounted$n, labels = percentdrivetrain, main = "Drivetrain Distribution", col = rainbow(nrow(drivetrainCounted)))
legend("right", c("all", "front", "rear"), cex = 0.8,
fill = rainbow(nrow(drivetrainCounted)))
# Although most vehicles are front wheel drive there is enough all and real wheel drive to gather some promising insights
# 12) Number of cars with same price
ggplot(cars_edited, aes(x = price_usd), stat = "count") + geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# This graph is extremely left-skewed. The sheer usefulness of price in our dataset will make this our main response attribute.
# 13) Pie Graph exchangeability Distribution
exchangeableGrouped <- group_by(cars_edited, is_exchangeable)
exchangeableCounted <- count(exchangeableGrouped)
percentexchangeable <- paste0(round(100*exchangeableCounted$n/sum(exchangeableCounted$n), 2), "%")
pie(exchangeableCounted$n, labels = percentexchangeable, main = "Exchangeability Distribution", col = rainbow(nrow(exchangeableCounted)))
legend("right", c("False", "True"), cex = 0.8,
fill = rainbow(nrow(exchangeableCounted)))
# Exchangeability is more common than anticipated. It will be interesting to see if pricier or cheaper cars consent to exchanges# 14) Pie Graph Location region: Count the number of cars in a region
regionPriceDF <- group_by(cars_edited, location_region)
regionPriceDFCount <- count(regionPriceDF)
percentRegion <- paste0(round(100*regionPriceDFCount$n/sum(regionPriceDFCount$n), 2), "%")
pie(regionPriceDFCount$n, labels = percentRegion, main = "Region Price Distribution", col = rainbow(nrow(regionPriceDFCount)))
legend("right", c("Brest Region", "Gomel Region", "Grodno Region", "Minsk Region", "Mogilev Region", "Vitebsk Region"), cex = 0.8,
fill = rainbow(nrow(regionPriceDFCount)))
# Minsk accounts for a very large amount of vehicles(makes sense considering the population sizes) with even distributions everywhere else.
# The usefulness of the attribute may be less since Minsk is such a large portion of the data.# 15) Histogram Number of photos: Graph to see how the data is skewed
ggplot(cars_edited) + geom_histogram(mapping = aes(number_of_photos))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Graph is very left-skewed. I suspect photos may increase the value of a vehicle, but tests will need to be done on this
# 16) Box plot Number of photos: Graph to see how the data is skewed
ggplot(cars_edited) + geom_boxplot(mapping = aes(number_of_photos))
# There are many outliers. With extra time we may be able to investigate the impact of these outliers on the data.
# 17) Histogram Up counter: investigating how our outliers look with our modifications
ggplot(cars_edited) + geom_histogram(mapping = aes(up_counter))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Clearly an outliers exists since there is a large scale.
# 18) Box plot Duration listed: investigating how our outliers look with our modifications
ggplot(cars_edited) + geom_boxplot(mapping = aes(duration_listed))
# There is a significant amount of outliers. There is no evidence to conclude these should be eliminated.
# 19) Histogram Duration listed: Graph to see how the data is skewed
ggplot(cars_edited) + geom_histogram(aes(duration_listed))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Once a general understanding of each attribute was gained we began to visualize various combinations of attributions. These visualizations were vital in seeing different relationships among our samples. The visualizations that were used were Balloon Plot, Scatterplot, frequency polygons, dplyr::summarize, and Boxplots.
# 1) Graph to show the amount of cars(by manufacturer name) in a region BALLOON PLOT
ggplot(cars_edited, aes(location_region, manufacturer_name)) + geom_count()
# Due to the quantity of categories a test will need to be done to gather significant data# 2) Graph to show the price of a car according to its year produced SCATTER PLOT
ggplot(cars_edited, aes(year_produced, price_usd)) + geom_point() + geom_smooth()## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
# There exists a parabolic relationship between year_produced and price_usd
# 3) Graph to show the number of cars in specific colors(10 red cars, 8 blue cars etc.) by region BAR GRAPH
ggplot(cars_edited, aes(color)) + geom_bar(aes(fill = location_region))
# From looking at the bar graph there does not seem to be any significant differences in color distribution for locations
# 4) Graph to show the price of a car according to it's millage(odometer) SCATTER PLOT
ggplot(cars_edited, aes(odometer_value, price_usd)) + geom_point(aes(color = is_exchangeable)) + geom_smooth()## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
# This graph is incredible diverse and indicates that there is a need for advanced models to access price relationships.
# 5)Graph to show the price of a car according to it's year produced AND body type SCATTER PLOT
ggplot(cars_edited, aes(year_produced, price_usd)) + geom_point(aes(color = body_type)) + geom_smooth()## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
# There seems to be a parabolic relationship between year_produced and price_usd
# 6) Group by car body type and get it's mean price
group_by(cars_edited, body_type) %>% summarise(price_mean = mean(price_usd))## # A tibble: 12 x 2
## body_type price_mean
## * <chr> <dbl>
## 1 cabriolet 10976.
## 2 coupe 7458.
## 3 hatchback 4036.
## 4 liftback 7873.
## 5 limousine 8154.
## 6 minibus 8466.
## 7 minivan 6131.
## 8 pickup 11748.
## 9 sedan 5782.
## 10 suv 13768.
## 11 universal 5017.
## 12 van 6675.# 7) Graph to show the outliers with body type and price BOX PLOT
ggplot(cars_edited) + geom_boxplot(mapping = aes(x = reorder(body_type, price_usd), y =
price_usd))
# 8) Graph to show the correlation between car body type, price, AND engine fuel
ggplot(cars_edited) + geom_point(mapping = aes(x = body_type, y = price_usd, color = engine_fuel))
# 9) Graph to show the price of a car according to it's number of photos incl. engine fuel SCATTER PLOT
ggplot(cars_edited) + geom_point(mapping = aes(x = number_of_photos, y = price_usd, color = engine_fuel))
# 10) Group cars by manufacturer, and get it's mean price
cars_edited %>% group_by(manufacturer_name) %>% summarize(mean(price_usd)) %>% View()Several machine learning techniques were used in an attempt to create the most accurate prediction model. The models used in the project were as follows:
Multiple Linear Regression: This was used for the sake of gauging the impact of the continuous attributes on the price of the vehicle. This model is simple to understand and involves less computing power than more advance models which led to our decision to utilize this ML method as our first model.
SVR: Used to see if there is a model that can handle every attribute and adjust the model according to its impact on price. Although this model involves much more computing power we believed that the robust nature of this model lends itself to a high level of accuracy. This models effectiveness with categorical data, and ability to work with both linear and non-linear boundaries makes it a prime model for our dataset. Furthermore, in order to optimize our SVR model we will use linear, polynomial, and radial kernel transformations and compare the results from the models that we generate.
Decision Tree (Partition): Used for predictive modeling. Since it is incredibly robust and relies on very few assumptions we believed it would be able to handle possible outliers in our data and work around the size of our dataset to produce an optimal model. Its simpler nature also makes it a model that would be preferred over other models(such as Random Forest Regression or KNN).
Random Forest Regression: Albeit, Random Forest Regression is more complicated than a Decision Tree since it leverages multiple decision trees it is still a crucial model for our dataset. The reason for using this model is for the sake of seeing if we can create an even more accurate model. If we find the accuracy is marginally better it may be preferred to use a Decision Tree since its easier to compute. Nevertheless, the Random Forest Regression is a wonderful tool in creating a predictive model and worth testing out for the sake of optimization.
KNN:K Nearest Neighbor was chosen due to its popularity and simplicity. KNN is able to handle our Categorical as well as Continuous attributes and for that reason it is a good model to be used to predict price. It will be vital to compare this model with the other models to investigate how useful of a model it is for our dataset.
Final outcomes and Analysis
Questions and Answer Justifications
Q1: What impact does a region have on price?
A: Although the region does impact price the extent of this impact would have to be assessed in a model that includes more attributes. The reason for this is because correlation does not necessitate causation. In other words, more attributes may be at play and to get a better understanding of the impact of region on price it will be vital to access the role region has in the overall models.
Justification:
To answer this question we decided to use a bar chart in order to see the average prices per region in comparison with each other.
Looking at the graph we can predict that region may play a part in price. We derive this prediction from the observation that although most regions have similar vehicle price averages Minsk has a significantly higher average. However, we can’t conclude that with just this graph, we need to confirm this by an appropriate test. The test used to see if region did in fact have a significant impact of price was the One-Way Anova Test. Anova was used because the region attribute consists of several categories and we wished to see its impact on a single continuous variable.
Prior to doing the One-Way Anova test we first performed some data transformation to gain a better understanding of the differences between the region prices.
group_by(regionPriceDF, regionPriceDF$location_region) %>%
summarise(
count = n(),
mean = mean(price_usd, na.rm = TRUE),
sd = sd(price_usd, na.rm = TRUE)
)## # A tibble: 6 x 4
## `regionPriceDF$location_region` count mean sd
## * <chr> <int> <dbl> <dbl>
## 1 Brest Region 2989 5091. 4652.
## 2 Gomel Region 3140 5022. 4603.
## 3 Grodno Region 2485 4745. 4223.
## 4 Minsk Region 24193 7668. 7117.
## 5 Mogilev Region 2678 4622. 4654.
## 6 Vitebsk Region 3005 4870. 4450.Once again quick inspection tells us that Minsk has a significantly different price and a much larger count. To affirm our suspicions from the graph and summarize we now perform the Anova test.
The hypotheses for the test are as follows:
H0 = The means of the different groups are the same
Ha = At least one sample mean is not equal to the others
Furthermore, we will use 0.05 as our significance level. The result of the anova test was the following:
# Compute the analysis of variance
res.aov <- aov(regionPriceDF$price_usd ~ regionPriceDF$location_region,
data = regionPriceDF)
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## regionPriceDF$location_region 5 7.020e+10 1.404e+10 355.9 <2e-16 ***
## Residuals 38484 1.518e+12 3.945e+07
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Looking at the results of the one-way anova we can confirm our prediction. The p-value was less than 0.05(<2e-16) and so we conclude that there are significant differences between the regions.
We continue our investigation of the problem at hand by using Tukey HSD to do multiple pairwise-comparisons between the means of our groups.
# Tukey Test
TukeyHSD(res.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = regionPriceDF$price_usd ~ regionPriceDF$location_region, data = regionPriceDF)
##
## $`regionPriceDF$location_region`
## diff lwr upr p adj
## Gomel Region-Brest Region -69.35261 -526.7479 388.042672 0.9980976
## Grodno Region-Brest Region -346.16487 -832.0693 139.739598 0.3250300
## Minsk Region-Brest Region 2576.92905 2229.9066 2923.951471 0.0000000
## Mogilev Region-Brest Region -468.67476 -944.9226 7.573076 0.0567405
## Vitebsk Region-Brest Region -220.94393 -683.3226 241.434780 0.7500113
## Grodno Region-Gomel Region -276.81226 -757.3836 203.759111 0.5708661
## Minsk Region-Gomel Region 2646.28166 2306.7669 2985.796388 0.0000000
## Mogilev Region-Gomel Region -399.32215 -870.1275 71.483215 0.1502298
## Vitebsk Region-Gomel Region -151.59132 -608.3623 305.179697 0.9345414
## Minsk Region-Grodno Region 2923.09392 2546.0489 3300.138960 0.0000000
## Mogilev Region-Grodno Region -122.50989 -621.0582 376.038406 0.9819607
## Vitebsk Region-Grodno Region 125.22095 -360.0959 610.537820 0.9775888
## Mogilev Region-Minsk Region -3045.60381 -3410.1197 -2681.087958 0.0000000
## Vitebsk Region-Minsk Region -2797.87298 -3144.0722 -2451.673795 0.0000000
## Vitebsk Region-Mogilev Region 247.73083 -227.9175 723.379142 0.6744633Analyzing the results we can conclude that the differences in average price between Minsk and every other region is statistically significant. The data set region does have an impact on vehicle price.
Q2: What is the distribution of manufacturers and whether manufacturers have a significant impact on the asking price of a vehicle?
A: We can confirm that there is a relationship between the manufacturer and asking price. The relationship seems to be one of the larger factor for asking price, but is not significant enough to predict price on its own.
Justification:
Two bar graphs were used in solving this question. First a bar graph was used to plot the distribution of vehicles for each manufacturer. Then another bar graph was used as a means of quickly inspecting how average price ranged between different manufacturers. As useful as these graph were in understanding the relationship between price and manufacturer it proved to be insufficient in proving a result. In order to prove that there existed a significant relationship between manufacturer and asking price of a car One-Way Anova was utilized. Once again were were dealing with a categorical data with several categories which lends the problem nicely to this sort of test.
First a bar graph was used to effectively plot the distribution of vehicles for each manufacturer.
# 1)What is the distribution of manufacturers?
ggplot(cars_edited, aes(y = manufacturer_name)) + geom_bar(aes(fill = manufacturer_name)) + geom_text(stat='count', aes(label=..count..), hjust=1)
Once the distribution of the Manufacturers was plotted we turned our attention onto the second part of the question: Whether manufacturers have a significant impact on the asking price of a vehicle?
Another bar graph was used as a means of quickly inspecting how average price ranged between different manufacturers.
manuPriceDF <- group_by(cars_edited, manufacturer_name)
manuPriceDF_averages <- summarise(manuPriceDF, average_price_usd = mean(price_usd))
ggplot(manuPriceDF_averages, aes(x = average_price_usd, y = manufacturer_name)) + geom_bar(aes(fill = manufacturer_name),stat="identity") + geom_text(aes(label = paste0("$",round(average_price_usd)), hjust = 1))
Looking at the bar graph we can predict that the manufacturer of a vehicle has an impact on asking price. Nevertheless, observation is not sufficient evidence and so we proceed by testing this claim. Once again we are dealing with categorical data and we wish to compare the means of the prices for the Manufacturers. Naturally we chose the One-Way Anova test to test our claim.
The hypotheses for the test are as follows:
H0 = The mean prices of the different groups are the same
Ha = At least one sample mean price is not equal to the others
Furthermore, we will use 0.05 as our significance level. The result of the anova test was the following:
manuSumm <- group_by(manuPriceDF, manuPriceDF$manufacturer_name) %>%
summarise(
count = n(),
mean = mean(price_usd, na.rm = TRUE),
sd = sd(price_usd, na.rm = TRUE)
)
# Compute the analysis of variance
res.aovTwo <- aov(manuPriceDF$price_usd ~ manuPriceDF$manufacturer_name,
data = manuPriceDF)
summary(res.aovTwo)## Df Sum Sq Mean Sq F value Pr(>F)
## manuPriceDF$manufacturer_name 54 2.917e+11 5.402e+09 160.1 <2e-16 ***
## Residuals 38435 1.297e+12 3.374e+07
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Investigating the results of the one-way anova we can confirm our claim. The p-value was less than 0.05(<2e-16) and so we conclude that there are significant differences between the manufacturers.
We continue with the Tukey HSD to do multiple pairwise-comparisons between the means of our groups. This will allow us to see exactly which manufacturers significantly differ in asking price.
# Tukey Test
TukeyHSD(res.aovTwo)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = manuPriceDF$price_usd ~ manuPriceDF$manufacturer_name, data = manuPriceDF)
##
## $`manuPriceDF$manufacturer_name`
## diff lwr upr p adj
## Alfa Romeo-Acura -10084.335812 -13398.412816 -6770.258809 0.0000000
## Audi-Acura -5617.940986 -8542.074322 -2693.807650 0.0000000
## AvtoVAZ-Acura -11253.964620 -14331.390271 -8176.538970 0.0000000
## BMW-Acura -3220.773360 -6142.847952 -298.698768 0.0098274
## Buick-Acura 103.433240 -4371.192712 4578.059192 1.0000000
## Cadillac-Acura -1679.761258 -6274.337531 2914.815015 1.0000000
## Chery-Acura -8226.907806 -12446.430142 -4007.385470 0.0000000
## Chevrolet-Acura -3899.753914 -6996.282248 -803.225579 0.0004993
## Chrysler-Acura -7777.394129 -10886.804139 -4667.984118 0.0000000
## Citroen-Acura -8337.560393 -11283.736949 -5391.383838 0.0000000
## Dacia-Acura -7430.134384 -11630.580762 -3229.688005 0.0000000
## Daewoo-Acura -11196.277176 -14484.878716 -7907.675636 0.0000000
## Dodge-Acura -7164.873956 -10355.247478 -3974.500435 0.0000000
## Fiat-Acura -9762.865630 -12762.013245 -6763.718015 0.0000000
## Ford-Acura -7775.208586 -10697.930086 -4852.487086 0.0000000
## GAZ-Acura -8883.154009 -12211.222572 -5555.085446 0.0000000
## Geely-Acura -5003.653374 -9012.297969 -995.008779 0.0006279
## Great Wall-Acura -6349.125631 -11206.653264 -1491.597998 0.0001830
## Honda-Acura -6257.789435 -9260.701961 -3254.876909 0.0000000
## Hyundai-Acura -4846.695515 -7816.604124 -1876.786905 0.0000001
## Infiniti-Acura 1021.719029 -2401.828492 4445.266551 1.0000000
## Iveco-Acura -2720.568787 -6225.148759 784.011185 0.5590565
## Jaguar-Acura 5040.114091 715.951749 9364.276432 0.0031536
## Jeep-Acura -1860.396470 -5529.818019 1809.025079 0.9993157
## Kia-Acura -4616.790383 -7605.188489 -1628.392276 0.0000007
## LADA-Acura -5174.204539 -8651.630940 -1696.778139 0.0000028
## Lancia-Acura -9871.377540 -13653.199454 -6089.555625 0.0000000
## Land Rover-Acura 2422.414471 -941.365166 5786.194108 0.7538364
## Lexus-Acura 4357.674936 1054.901794 7660.448078 0.0001401
## Lifan-Acura -4492.349739 -8966.975691 -17.723787 0.0471910
## Lincoln-Acura -3035.413687 -7892.941320 1822.113946 0.9561928
## Mazda-Acura -8041.358808 -10998.001161 -5084.716455 0.0000000
## Mercedes-Benz-Acura -3383.068837 -6311.131868 -455.005806 0.0038085
## Mini-Acura 360.814973 -3690.205026 4411.834972 1.0000000
## Mitsubishi-Acura -7353.423719 -10344.779418 -4362.068021 0.0000000
## Moskvitch-Acura -11793.973727 -16074.308968 -7513.638487 0.0000000
## Nissan-Acura -6361.371736 -9316.316508 -3406.426964 0.0000000
## Opel-Acura -8346.893009 -11267.007102 -5426.778917 0.0000000
## Peugeot-Acura -8390.668183 -11325.931250 -5455.405115 0.0000000
## Pontiac-Acura -8409.341861 -13036.919107 -3781.764616 0.0000000
## Porsche-Acura 5856.937861 1693.008174 10020.867549 0.0000196
## Renault-Acura -8213.662237 -11137.428803 -5289.895672 0.0000000
## Rover-Acura -11038.643696 -14304.640542 -7772.646850 0.0000000
## Saab-Acura -8642.300539 -12305.235359 -4979.365718 0.0000000
## Seat-Acura -8904.870563 -12089.495659 -5720.245467 0.0000000
## Skoda-Acura 953.187554 -2021.345703 3927.720810 1.0000000
## SsangYong-Acura -5053.563504 -8963.204678 -1143.922330 0.0002493
## Subaru-Acura -5250.791304 -8447.139313 -2054.443295 0.0000000
## Suzuki-Acura -7470.287148 -10737.813833 -4202.760464 0.0000000
## Toyota-Acura -2974.733453 -5935.978455 -13.488451 0.0467818
## UAZ-Acura -9329.762125 -13299.068324 -5360.455927 0.0000000
## Volkswagen-Acura -6345.125089 -9253.294780 -3436.955397 0.0000000
## Volvo-Acura -4356.544370 -7371.536467 -1341.552272 0.0000079
## ZAZ-Acura -11412.674719 -16040.251964 -6785.097473 0.0000000
## Audi-Alfa Romeo 4466.394826 2769.939003 6162.850650 0.0000000
## AvtoVAZ-Alfa Romeo -1169.628808 -3118.463133 779.205518 0.9771695
## BMW-Alfa Romeo 6863.562452 5170.657696 8556.467208 0.0000000
## Buick-Alfa Romeo 10187.769052 6399.671376 13975.866728 0.0000000
## Cadillac-Alfa Romeo 8404.574555 4475.511342 12333.637767 0.0000000
## Chery-Alfa Romeo 1857.428007 -1625.641808 5340.497822 0.9977701
## Chevrolet-Alfa Romeo 6184.581899 4205.720025 8163.443773 0.0000000
## Chrysler-Alfa Romeo 2306.941684 307.982654 4305.900714 0.0039024
## Citroen-Alfa Romeo 1746.775419 12.600431 3480.950407 0.0449530
## Dacia-Alfa Romeo 2654.201429 -805.734463 6114.137320 0.5923139
## Daewoo-Alfa Romeo -1111.941364 -3379.606834 1155.724106 0.9997048
## Dodge-Alfa Romeo 2919.461856 796.753728 5042.169985 0.0000403
## Fiat-Alfa Romeo 321.470183 -1501.245651 2144.186016 1.0000000
## Ford-Alfa Romeo 2309.127227 615.106106 4003.148348 0.0000532
## GAZ-Alfa Romeo 1201.181803 -1123.349753 3525.713359 0.9989183
## Geely-Alfa Romeo 5080.682439 1856.300625 8305.064253 0.0000003
## Great Wall-Alfa Romeo 3735.210181 -498.344035 7968.764397 0.2232242
## Honda-Alfa Romeo 3826.546378 1997.642271 5655.450484 0.0000000
## Hyundai-Alfa Romeo 5237.640298 3463.446469 7011.834126 0.0000000
## Infiniti-Alfa Romeo 11106.054842 8646.769534 13565.340149 0.0000000
## Iveco-Alfa Romeo 7363.767026 4792.874695 9934.659356 0.0000000
## Jaguar-Alfa Romeo 15124.449903 11515.324449 18733.575358 0.0000000
## Jeep-Alfa Romeo 8223.939343 5432.512987 11015.365698 0.0000000
## Kia-Alfa Romeo 5467.545430 3662.571791 7272.519068 0.0000000
## LADA-Alfa Romeo 4910.131273 2376.378925 7443.883621 0.0000000
## Lancia-Alfa Romeo 212.958273 -2724.657016 3150.573562 1.0000000
## Land Rover-Alfa Romeo 12506.750284 10131.372411 14882.128157 0.0000000
## Lexus-Alfa Romeo 14442.010748 12153.841826 16730.179671 0.0000000
## Lifan-Alfa Romeo 5591.986074 1803.888398 9380.083750 0.0000037
## Lincoln-Alfa Romeo 7048.922126 2815.367910 11282.476341 0.0000000
## Mazda-Alfa Romeo 2042.977004 291.080719 3794.873290 0.0031196
## Mercedes-Benz-Alfa Romeo 6701.266975 4998.046583 8404.487367 0.0000000
## Mini-Alfa Romeo 10445.150786 7168.236136 13722.065436 0.0000000
## Mitsubishi-Alfa Romeo 2730.912093 921.045935 4540.778251 0.0000016
## Moskvitch-Alfa Romeo -1709.637915 -5266.135681 1846.859852 0.9998248
## Nissan-Alfa Romeo 3722.964077 1973.934289 5471.993864 0.0000000
## Opel-Alfa Romeo 1737.442803 47.924261 3426.961345 0.0327288
## Peugeot-Alfa Romeo 1693.667630 -21.901042 3409.236302 0.0600571
## Pontiac-Alfa Romeo 1674.993951 -2292.609577 5642.597479 0.9999961
## Porsche-Alfa Romeo 15941.273674 12525.762434 19356.784914 0.0000000
## Renault-Alfa Romeo 1870.673575 174.850023 3566.497128 0.0096789
## Rover-Alfa Romeo -954.307884 -3189.065587 1280.449819 0.9999944
## Saab-Alfa Romeo 1442.035274 -1340.858560 4224.929108 0.9988450
## Seat-Alfa Romeo 1179.465250 -934.593310 3293.523810 0.9941623
## Skoda-Alfa Romeo 11037.523366 9255.598933 12819.447799 0.0000000
## SsangYong-Alfa Romeo 5030.772308 1930.336741 8131.207876 0.0000001
## Subaru-Alfa Romeo 4833.544508 2701.867425 6965.221591 0.0000000
## Suzuki-Alfa Romeo 2614.048664 377.055765 4851.041563 0.0029825
## Toyota-Alfa Romeo 7109.602359 5349.949395 8869.255323 0.0000000
## UAZ-Alfa Romeo 754.573687 -2420.768544 3929.915919 1.0000000
## Volkswagen-Alfa Romeo 3739.210724 2070.421478 5407.999970 0.0000000
## Volvo-Alfa Romeo 5727.791443 3879.120585 7576.462301 0.0000000
## ZAZ-Alfa Romeo -1328.338906 -5295.942434 2639.264622 1.0000000
## AvtoVAZ-Audi -5636.023634 -6804.528923 -4467.518345 0.0000000
## BMW-Audi 2397.167626 1738.854709 3055.480543 0.0000000
## Buick-Audi 5721.374226 2269.254097 9173.494355 0.0000000
## Cadillac-Audi 3938.179728 331.936932 7544.422525 0.0116938
## Chery-Audi -2608.966820 -5723.324654 505.391015 0.3467819
## Chevrolet-Audi 1718.187072 500.261089 2936.113056 0.0000178
## Chrysler-Audi -2159.453143 -3409.767738 -909.138547 0.0000000
## Citroen-Audi -2719.619407 -3477.783159 -1961.455656 0.0000000
## Dacia-Audi -1812.193398 -4900.656730 1276.269935 0.9846435
## Daewoo-Audi -5578.336190 -7224.469862 -3932.202518 0.0000000
## Dodge-Audi -1546.932970 -2986.842406 -107.023535 0.0157332
## Fiat-Audi -4144.924644 -5088.186216 -3201.663072 0.0000000
## Ford-Audi -2157.267600 -2818.446050 -1496.089150 0.0000000
## GAZ-Audi -3265.213023 -4988.841851 -1541.584195 0.0000000
## Geely-Audi 614.287612 -2207.783873 3436.359097 1.0000000
## Great Wall-Audi -731.184645 -4666.973090 3204.603800 1.0000000
## Honda-Audi -639.848449 -1595.013155 315.316257 0.8863305
## Hyundai-Audi 771.245471 -74.457269 1616.948211 0.1598496
## Infiniti-Audi 6639.660015 4738.204360 8541.115670 0.0000000
## Iveco-Audi 2897.372199 853.616647 4941.127752 0.0000151
## Jaguar-Audi 10658.055077 7403.329907 13912.780247 0.0000000
## Jeep-Audi 3757.544516 1442.482858 6072.606174 0.0000001
## Kia-Audi 1001.150603 92.646767 1909.654439 0.0098688
## LADA-Audi 443.736447 -1553.098635 2440.571529 1.0000000
## Lancia-Audi -4253.436553 -6742.820608 -1764.052499 0.0000000
## Land Rover-Audi 8040.355457 6248.744612 9831.966303 0.0000000
## Lexus-Audi 9975.615922 8301.349992 11649.881852 0.0000000
## Lifan-Audi 1125.591247 -2326.528882 4577.711376 1.0000000
## Lincoln-Audi 2582.527299 -1353.261146 6518.315744 0.9124986
## Mazda-Audi -2423.417822 -3221.283712 -1625.551932 0.0000000
## Mercedes-Benz-Audi 2234.872149 1550.467964 2919.276334 0.0000000
## Mini-Audi 5978.755959 3096.808873 8860.703045 0.0000000
## Mitsubishi-Audi -1735.482733 -2653.668387 -817.297080 0.0000000
## Moskvitch-Audi -6176.032741 -9372.300212 -2979.765270 0.0000000
## Nissan-Audi -743.430750 -1534.982758 48.121259 0.1145293
## Opel-Audi -2728.952023 -3378.507478 -2079.396569 0.0000000
## Peugeot-Audi -2772.727196 -3487.307668 -2058.146725 0.0000000
## Pontiac-Audi -2791.400875 -6439.595927 856.794177 0.5994716
## Porsche-Audi 11474.878847 8436.266267 14513.491428 0.0000000
## Renault-Audi -2595.721251 -3261.504178 -1929.938325 0.0000000
## Rover-Audi -5420.702710 -7021.199938 -3820.205483 0.0000000
## Saab-Audi -3024.359553 -5329.125804 -719.593301 0.0001637
## Seat-Audi -3286.929576 -4714.057104 -1859.802049 0.0000000
## Skoda-Audi 6571.128540 5709.325738 7432.931341 0.0000000
## SsangYong-Audi 564.377482 -2115.202726 3243.957690 1.0000000
## Subaru-Audi 367.149682 -1085.949272 1820.248636 1.0000000
## Suzuki-Audi -1852.346162 -3455.962891 -248.729434 0.0038289
## Toyota-Audi 2643.207533 1828.451166 3457.963900 0.0000000
## UAZ-Audi -3711.821139 -6477.729249 -945.913029 0.0000853
## Volkswagen-Audi -727.184102 -1320.738592 -133.629613 0.0009995
## Volvo-Audi 1261.396617 268.908166 2253.885067 0.0003931
## ZAZ-Audi -5794.733733 -9442.928785 -2146.538680 0.0000002
## BMW-AvtoVAZ 8033.191260 6869.847475 9196.535045 0.0000000
## Buick-AvtoVAZ 11357.397860 7774.504301 14940.291419 0.0000000
## Cadillac-AvtoVAZ 9574.203362 5842.584414 13305.822310 0.0000000
## Chery-AvtoVAZ 3027.056814 -231.657286 6285.770914 0.1302828
## Chevrolet-AvtoVAZ 7354.210706 5803.940870 8904.480543 0.0000000
## Chrysler-AvtoVAZ 3476.570491 1900.728033 5052.412950 0.0000000
## Citroen-AvtoVAZ 2916.404227 1693.781987 4139.026466 0.0000000
## Dacia-AvtoVAZ 3823.830236 589.854568 7057.805905 0.0022971
## Daewoo-AvtoVAZ 57.687444 -1847.502555 1962.877443 1.0000000
## Dodge-AvtoVAZ 4089.090664 2358.965799 5819.215529 0.0000000
## Fiat-AvtoVAZ 1491.098990 145.834979 2836.363001 0.0088312
## Ford-AvtoVAZ 3478.756034 2313.788306 4643.723763 0.0000000
## GAZ-AvtoVAZ 2370.810611 398.276909 4343.344313 0.0015784
## Geely-AvtoVAZ 6250.311246 3269.693805 9230.928688 0.0000000
## Great Wall-AvtoVAZ 4904.838989 853.860891 8955.817086 0.0013274
## Honda-AvtoVAZ 4996.175185 3642.538424 6349.811947 0.0000000
## Hyundai-AvtoVAZ 6407.269105 5128.517374 7686.020837 0.0000000
## Infiniti-AvtoVAZ 12275.683649 10146.006098 14405.361201 0.0000000
## Iveco-AvtoVAZ 8533.395833 6275.757770 10791.033896 0.0000000
## Jaguar-AvtoVAZ 16294.078711 12900.963800 19687.193622 0.0000000
## Jeep-AvtoVAZ 9393.568150 6887.674428 11899.461873 0.0000000
## Kia-AvtoVAZ 6637.174237 5316.048927 7958.299548 0.0000000
## LADA-AvtoVAZ 6079.760081 3864.507677 8295.012485 0.0000000
## Lancia-AvtoVAZ 1382.587081 -1285.188367 4050.362528 0.9988410
## Land Rover-AvtoVAZ 13676.379091 11644.172830 15708.585353 0.0000000
## Lexus-AvtoVAZ 15611.639556 13682.090575 17541.188537 0.0000000
## Lifan-AvtoVAZ 6761.614881 3178.721323 10344.508440 0.0000000
## Lincoln-AvtoVAZ 8218.550933 4167.572836 12269.529031 0.0000000
## Mazda-AvtoVAZ 3212.605812 1964.974919 4460.236705 0.0000000
## Mercedes-Benz-AvtoVAZ 7870.895783 6692.591088 9049.200478 0.0000000
## Mini-AvtoVAZ 11614.779593 8577.410358 14652.148829 0.0000000
## Mitsubishi-AvtoVAZ 3900.540901 2572.739048 5228.342753 0.0000000
## Moskvitch-AvtoVAZ -540.009107 -3877.091458 2797.073243 1.0000000
## Nissan-AvtoVAZ 4892.592884 3648.990276 6136.195493 0.0000000
## Opel-AvtoVAZ 2907.071611 1748.660995 4065.482226 0.0000000
## Peugeot-AvtoVAZ 2863.296438 1667.211944 4059.380932 0.0000000
## Pontiac-AvtoVAZ 2844.622759 -927.554332 6616.799849 0.6390892
## Porsche-AvtoVAZ 17110.902482 13924.500234 20297.304729 0.0000000
## Renault-AvtoVAZ 3040.302383 1872.715225 4207.889540 0.0000000
## Rover-AvtoVAZ 215.320924 -1650.579460 2081.221307 1.0000000
## Saab-AvtoVAZ 2611.664081 115.278626 5108.049537 0.0248018
## Seat-AvtoVAZ 2349.094058 629.592431 4068.595684 0.0000497
## Skoda-AvtoVAZ 12207.152174 10917.696112 13496.608235 0.0000000
## SsangYong-AvtoVAZ 6200.401116 3354.326107 9046.476125 0.0000000
## Subaru-AvtoVAZ 6003.173316 4262.056020 7744.290611 0.0000000
## Suzuki-AvtoVAZ 3783.677472 1915.100613 5652.254331 0.0000000
## Toyota-AvtoVAZ 8279.231167 7020.731742 9537.730592 0.0000000
## UAZ-AvtoVAZ 1924.202495 -1003.294822 4851.699811 0.9105272
## Volkswagen-AvtoVAZ 4908.839532 3780.876856 6036.802207 0.0000000
## Volvo-AvtoVAZ 6897.420251 5517.193389 8277.647113 0.0000000
## ZAZ-AvtoVAZ -158.710099 -3930.887189 3613.466992 1.0000000
## Buick-BMW 3324.206600 -126.169834 6774.583034 0.0834380
## Cadillac-BMW 1541.012102 -2063.561556 5145.585761 0.9999941
## Chery-BMW -5006.134445 -8118.559364 -1893.709527 0.0000001
## Chevrolet-BMW -678.980553 -1891.955348 533.994241 0.9937579
## Chrysler-BMW -4556.620768 -5802.112936 -3311.128601 0.0000000
## Citroen-BMW -5116.787033 -5866.971295 -4366.602772 0.0000000
## Dacia-BMW -4209.361023 -7295.875224 -1122.846823 0.0000524
## Daewoo-BMW -7975.503816 -9617.977627 -6333.030005 0.0000000
## Dodge-BMW -3944.100596 -5379.824572 -2508.376620 0.0000000
## Fiat-BMW -6542.092270 -7478.952209 -5605.232331 0.0000000
## Ford-BMW -4554.435225 -5206.448335 -3902.422116 0.0000000
## GAZ-BMW -5662.380649 -7382.514507 -3942.246791 0.0000000
## Geely-BMW -1782.880014 -4602.818243 1037.058216 0.9477997
## Great Wall-BMW -3128.352271 -7062.611392 805.906850 0.4908723
## Honda-BMW -3037.016075 -3985.859458 -2088.172691 0.0000000
## Hyundai-BMW -1625.922155 -2464.478817 -787.365492 0.0000000
## Infiniti-BMW 4242.492389 2344.204276 6140.780502 0.0000000
## Iveco-BMW 500.204573 -1540.604313 2541.013460 1.0000000
## Jaguar-BMW 8260.887451 5008.011787 11513.763115 0.0000000
## Jeep-BMW 1360.376890 -952.083841 3672.837622 0.9839119
## Kia-BMW -1396.017023 -2297.872539 -494.161506 0.0000006
## LADA-BMW -1953.431179 -3947.250252 40.387894 0.0667270
## Lancia-BMW -6650.604179 -9137.569624 -4163.638735 0.0000000
## Land Rover-BMW 5643.187832 3854.939087 7431.436577 0.0000000
## Lexus-BMW 7578.448296 5907.780599 9249.115994 0.0000000
## Lifan-BMW -1271.576379 -4721.952813 2178.800056 1.0000000
## Lincoln-BMW 185.359673 -3748.899448 4119.618795 1.0000000
## Mazda-BMW -4820.585448 -5610.872820 -4030.298076 0.0000000
## Mercedes-Benz-BMW -162.295477 -837.849501 513.258547 1.0000000
## Mini-BMW 3581.588333 701.730150 6461.446517 0.0006887
## Mitsubishi-BMW -4132.650359 -5044.258304 -3221.042414 0.0000000
## Moskvitch-BMW -8573.200367 -11767.584486 -5378.816248 0.0000000
## Nissan-BMW -3140.598375 -3924.510828 -2356.685923 0.0000000
## Opel-BMW -5126.119649 -5766.343392 -4485.895907 0.0000000
## Peugeot-BMW -5169.894822 -5876.003456 -4463.786189 0.0000000
## Pontiac-BMW -5188.568501 -8835.113618 -1542.023384 0.0000134
## Porsche-BMW 9077.711222 6041.079771 12114.342672 0.0000000
## Renault-BMW -4992.888877 -5649.570731 -4336.207024 0.0000000
## Rover-BMW -7817.870336 -9414.603102 -6221.137570 0.0000000
## Saab-BMW -5421.527178 -7723.680872 -3119.373485 0.0000000
## Seat-BMW -5684.097202 -7107.001672 -4261.192732 0.0000000
## Skoda-BMW 4173.960914 3319.169582 5028.752245 0.0000000
## SsangYong-BMW -1832.790144 -4510.123564 844.543276 0.8534099
## Subaru-BMW -2030.017944 -3478.969538 -581.066350 0.0000223
## Suzuki-BMW -4249.513788 -5849.373396 -2649.654181 0.0000000
## Toyota-BMW 246.039907 -561.296509 1053.376323 1.0000000
## UAZ-BMW -6108.988765 -8872.720269 -3345.257262 0.0000000
## Volkswagen-BMW -3124.351728 -3707.679324 -2541.024133 0.0000000
## Volvo-BMW -1135.771009 -2122.177354 -149.364665 0.0040970
## ZAZ-BMW -8191.901358 -11838.446475 -4545.356241 0.0000000
## Cadillac-Buick -1783.194498 -6730.588635 3164.199640 1.0000000
## Chery-Buick -8330.341045 -12931.530713 -3729.151377 0.0000000
## Chevrolet-Buick -4003.187154 -7602.501719 -403.872588 0.0082659
## Chrysler-Buick -7880.827368 -11491.230148 -4270.424589 0.0000000
## Citroen-Buick -8440.993633 -11911.805342 -4970.181925 0.0000000
## Dacia-Buick -7533.567624 -12117.269991 -2949.865256 0.0000000
## Daewoo-Buick -11299.710416 -15065.540697 -7533.880134 0.0000000
## Dodge-Buick -7268.307196 -10948.668703 -3587.945689 0.0000000
## Fiat-Buick -9866.298870 -13382.186245 -6350.411495 0.0000000
## Ford-Buick -7878.641825 -11329.566134 -4427.717517 0.0000000
## GAZ-Buick -8986.587249 -12786.931704 -5186.242794 0.0000000
## Geely-Buick -5107.086614 -9515.692868 -698.480359 0.0036005
## Great Wall-Buick -6452.558871 -11645.068122 -1260.049620 0.0007028
## Honda-Buick -6361.222675 -9880.322170 -2842.123180 0.0000000
## Hyundai-Buick -4950.128755 -8441.107810 -1459.149699 0.0000150
## Infiniti-Buick 918.285789 -2965.945827 4802.517406 1.0000000
## Iveco-Buick -2824.002027 -6779.840547 1131.836494 0.7731917
## Jaguar-Buick 4936.680851 239.345695 9634.016008 0.0229918
## Jeep-Buick -1963.829710 -6066.418176 2138.758756 0.9998435
## Kia-Buick -4720.223623 -8226.945862 -1213.501384 0.0000780
## LADA-Buick -5277.637779 -9209.440417 -1345.835141 0.0000848
## Lancia-Buick -9974.810779 -14178.232554 -5771.389005 0.0000000
## Land Rover-Buick 2318.981232 -1512.675207 6150.637670 0.9737066
## Lexus-Buick 4254.241696 476.029415 8032.453978 0.0064846
## Lifan-Buick -4595.782979 -9431.985521 240.419563 0.0993122
## Lincoln-Buick -3138.846927 -8331.356178 2053.662324 0.9742250
## Mazda-Buick -8144.792048 -11624.491980 -4665.092116 0.0000000
## Mercedes-Benz-Buick -3486.502077 -6941.951502 -31.052652 0.0438249
## Mini-Buick 257.381733 -4189.790475 4704.553941 1.0000000
## Mitsubishi-Buick -7456.856959 -10966.099974 -3947.613945 0.0000000
## Moskvitch-Buick -11897.406967 -16554.428267 -7240.385667 0.0000000
## Nissan-Buick -6464.804976 -9943.062617 -2986.547334 0.0000000
## Opel-Buick -8450.326249 -11899.042522 -5001.609977 0.0000000
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## Pontiac-Buick -8512.775101 -13490.831828 -3534.718374 0.0000000
## Porsche-Buick 5753.504622 1203.242205 10303.767038 0.0004500
## Renault-Buick -8317.095477 -11768.904937 -4865.286017 0.0000000
## Rover-Buick -11142.076936 -14888.183317 -7395.970555 0.0000000
## Saab-Buick -8745.733779 -12842.521437 -4648.946120 0.0000000
## Seat-Buick -9008.303802 -12683.683323 -5332.924282 0.0000000
## Skoda-Buick 849.754314 -2645.159950 4344.668577 1.0000000
## SsangYong-Buick -5156.996744 -9475.778022 -838.215465 0.0018364
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## Suzuki-Buick -7573.720388 -11321.160615 -3826.280161 0.0000000
## Toyota-Buick -3078.166693 -6561.778264 405.444878 0.2200643
## UAZ-Buick -9433.195365 -13806.062767 -5060.327963 0.0000000
## Volkswagen-Buick -6448.558328 -9887.166851 -3009.949806 0.0000000
## Volvo-Buick -4459.977609 -7989.390445 -930.564774 0.0004573
## ZAZ-Buick -11516.107958 -16494.164686 -6538.051231 0.0000000
## Chery-Cadillac -6547.146548 -11265.069828 -1829.223267 0.0000304
## Chevrolet-Cadillac -2219.992656 -5967.380955 1527.395643 0.9817803
## Chrysler-Cadillac -6097.632871 -9855.672513 -2339.593229 0.0000001
## Citroen-Cadillac -6657.799135 -10281.938708 -3033.659563 0.0000000
## Dacia-Cadillac -5750.373126 -10451.243377 -1049.502874 0.0010376
## Daewoo-Cadillac -9516.515918 -13424.115108 -5608.916728 0.0000000
## Dodge-Cadillac -5485.112698 -9310.411974 -1659.813423 0.0000103
## Fiat-Cadillac -8083.104372 -11750.435521 -4415.773223 0.0000000
## Ford-Cadillac -6095.447328 -9700.545427 -2490.349228 0.0000000
## GAZ-Cadillac -7203.392751 -11144.264698 -3262.520804 0.0000000
## Geely-Cadillac -3323.892116 -7854.197054 1206.412822 0.7097684
## Great Wall-Cadillac -4669.364373 -9965.590015 626.861268 0.2247768
## Honda-Cadillac -4578.028177 -8248.438914 -907.617440 0.0006400
## Hyundai-Cadillac -3166.934257 -6810.392571 476.524057 0.2560161
## Infiniti-Cadillac 2701.480287 -1320.348771 6723.309345 0.8825204
## Iveco-Cadillac -1040.807529 -5131.835776 3050.220719 1.0000000
## Jaguar-Cadillac 6719.875349 1908.138522 11531.612176 0.0000248
## Jeep-Cadillac -180.635212 -4413.729334 4052.458910 1.0000000
## Kia-Cadillac -2937.029125 -6595.574544 721.516294 0.4637593
## LADA-Cadillac -3494.443281 -7562.234538 573.347976 0.2835284
## Lancia-Cadillac -8191.616282 -12522.506298 -3860.726265 0.0000000
## Land Rover-Cadillac 4102.175729 131.099706 8073.251753 0.0304689
## Lexus-Cadillac 6037.436194 2117.902833 9956.969555 0.0000007
## Lifan-Cadillac -2812.588481 -7759.982618 2134.805656 0.9915164
## Lincoln-Cadillac -1355.652429 -6651.878070 3940.573213 1.0000000
## Mazda-Cadillac -6361.597550 -9994.250209 -2728.944891 0.0000000
## Mercedes-Benz-Cadillac -1703.307579 -5312.737514 1906.122355 0.9998937
## Mini-Cadillac 2040.576231 -2527.267283 6608.419745 0.9999777
## Mitsubishi-Cadillac -5673.662462 -9334.624119 -2012.700804 0.0000006
## Moskvitch-Cadillac -10114.212469 -14886.601924 -5341.823014 0.0000000
## Nissan-Cadillac -4681.610478 -8312.881597 -1050.339358 0.0002677
## Opel-Cadillac -6667.131752 -10270.116299 -3064.147204 0.0000000
## Peugeot-Cadillac -6710.906925 -10326.180167 -3095.633683 0.0000000
## Pontiac-Cadillac -6729.580604 -11815.728704 -1643.432503 0.0001292
## Porsche-Cadillac 7536.699119 2868.429442 12204.968797 0.0000001
## Renault-Cadillac -6533.900979 -10139.846385 -2927.955574 0.0000000
## Rover-Cadillac -9358.882438 -13247.476881 -5470.287996 0.0000000
## Saab-Cadillac -6962.539281 -11190.011674 -2735.066887 0.0000000
## Seat-Cadillac -7225.109305 -11045.615599 -3404.603011 0.0000000
## Skoda-Cadillac 2632.948811 -1014.280196 6280.177819 0.7482850
## SsangYong-Cadillac -3373.802246 -7816.743328 1069.138835 0.6203061
## Subaru-Cadillac -3571.030046 -7401.313578 259.253485 0.1248968
## Suzuki-Cadillac -5790.525890 -9680.405320 -1900.646461 0.0000027
## Toyota-Cadillac -1294.972195 -4931.371967 2341.427577 1.0000000
## UAZ-Cadillac -7650.000867 -12145.534544 -3154.467191 0.0000000
## Volkswagen-Cadillac -4665.363831 -8258.674601 -1072.053060 0.0002206
## Volvo-Cadillac -2676.783112 -6357.083193 1003.516970 0.7308371
## ZAZ-Cadillac -9732.913461 -14819.061561 -4646.765360 0.0000000
## Chevrolet-Chery 4327.153892 1050.393804 7603.913980 0.0001366
## Chrysler-Chery 449.513677 -2839.422255 3738.449609 1.0000000
## Citroen-Chery -110.652588 -3245.716423 3024.411247 1.0000000
## Dacia-Chery 796.773422 -3538.248923 5131.795767 1.0000000
## Daewoo-Chery -2969.369370 -6428.208633 489.469892 0.2851084
## Dodge-Chery 1062.033849 -2303.549630 4427.617329 1.0000000
## Fiat-Chery -1535.957824 -4720.852700 1648.937051 0.9998090
## Ford-Chery 451.699220 -2661.333051 3564.731491 1.0000000
## GAZ-Chery -656.246203 -4152.631381 2840.138975 1.0000000
## Geely-Chery 3223.254432 -926.191367 7372.700231 0.5572329
## Great Wall-Chery 1877.782174 -3096.576443 6852.140792 0.9999999
## Honda-Chery 1969.118371 -1219.322093 5157.558834 0.9634981
## Hyundai-Chery 3380.212291 222.835833 6537.588749 0.0167363
## Infiniti-Chery 9248.626835 5661.239682 12836.013987 0.0000000
## Iveco-Chery 5506.339019 1841.540289 9171.137749 0.0000019
## Jaguar-Chery 13267.021897 8812.019213 17722.024580 0.0000000
## Jeep-Chery 6366.511336 2543.773645 10189.249027 0.0000000
## Kia-Chery 3610.117423 435.343069 6784.891776 0.0053133
## LADA-Chery 3052.703266 -586.137665 6691.544197 0.3430383
## Lancia-Chery -1644.469734 -5575.226143 2286.286675 0.9999970
## Land Rover-Chery 10649.322277 7118.928192 14179.716362 0.0000000
## Lexus-Chery 12584.582742 9112.266601 16056.898882 0.0000000
## Lifan-Chery 3734.558067 -866.631601 8335.747735 0.4328201
## Lincoln-Chery 5191.494119 217.135501 10165.852736 0.0258104
## Mazda-Chery 185.548997 -2959.352106 3330.450101 1.0000000
## Mercedes-Benz-Chery 4843.838969 1725.791172 7961.886765 0.0000005
## Mini-Chery 8587.722779 4397.325187 12778.120371 0.0000000
## Mitsubishi-Chery 873.484086 -2304.074390 4051.042562 1.0000000
## Moskvitch-Chery -3567.065922 -7979.541273 845.409429 0.4440685
## Nissan-Chery 1865.536070 -1277.769126 5008.841266 0.9811846
## Opel-Chery -119.985204 -3230.569596 2990.599189 1.0000000
## Peugeot-Chery -163.760377 -3288.570487 2961.049733 1.0000000
## Pontiac-Chery -182.434056 -4932.501436 4567.633324 1.0000000
## Porsche-Chery 14083.845667 9784.196915 18383.494419 0.0000000
## Renault-Chery 13.245568 -3100.767900 3127.259036 1.0000000
## Rover-Chery -2811.735891 -6249.090157 625.618375 0.4111389
## Saab-Chery -415.392733 -4231.904288 3401.118822 1.0000000
## Seat-Chery -677.962757 -4038.097569 2682.172055 1.0000000
## Skoda-Chery 9180.095359 6018.368451 12341.822268 0.0000000
## SsangYong-Chery 3173.344302 -880.538166 7227.226770 0.5354533
## Subaru-Chery 2976.116501 -395.130975 6347.363978 0.2220134
## Suzuki-Chery 756.620657 -2682.187216 4195.428530 1.0000000
## Toyota-Chery 5252.174352 2102.945731 8401.402974 0.0000000
## UAZ-Chery -1102.854320 -5214.309129 3008.600489 1.0000000
## Volkswagen-Chery 1881.782717 -1217.591398 4981.156832 0.9722772
## Volvo-Chery 3870.363436 670.543707 7070.183165 0.0013596
## ZAZ-Chery -3185.766913 -7935.834293 1564.300467 0.8846764
## Chrysler-Chevrolet -3877.640215 -5490.469575 -2264.810855 0.0000000
## Citroen-Chevrolet -4437.806480 -5707.745151 -3167.867808 0.0000000
## Dacia-Chevrolet -3530.380470 -6782.539401 -278.221539 0.0130492
## Daewoo-Chevrolet -7296.523262 -9232.417894 -5360.628631 0.0000000
## Dodge-Chevrolet -3265.120043 -5028.999613 -1501.240472 0.0000000
## Fiat-Chevrolet -5863.111716 -7251.518843 -4474.704590 0.0000000
## Ford-Chevrolet -3875.454672 -5089.987051 -2660.922293 0.0000000
## GAZ-Chevrolet -4983.400095 -6985.605952 -2981.194238 0.0000000
## Geely-Chevrolet -1103.899460 -4104.236012 1896.437091 1.0000000
## Great Wall-Chevrolet -2449.371718 -6514.880620 1616.137185 0.9756340
## Honda-Chevrolet -2358.035521 -3754.556760 -961.514282 0.0000000
## Hyundai-Chevrolet -946.941601 -2271.005452 377.122249 0.7692445
## Infiniti-Chevrolet 4921.472943 2764.283669 7078.662217 0.0000000
## Iveco-Chevrolet 1179.185127 -1104.423577 3462.793831 0.9989366
## Jaguar-Chevrolet 8939.868005 5529.418197 12350.317812 0.0000000
## Jeep-Chevrolet 2039.357444 -489.959151 4568.674039 0.4514558
## Kia-Chevrolet -717.036469 -2082.068035 647.995097 0.9984146
## LADA-Chevrolet -1274.450626 -3516.164769 967.263518 0.9915114
## Lancia-Chevrolet -5971.623626 -8661.412638 -3281.834614 0.0000000
## Land Rover-Chevrolet 6322.168385 4261.148885 8383.187885 0.0000000
## Lexus-Chevrolet 8257.428850 6297.556823 10217.300876 0.0000000
## Lifan-Chevrolet -592.595825 -4191.910391 3006.718740 1.0000000
## Lincoln-Chevrolet 864.340227 -3201.168676 4929.849130 1.0000000
## Mazda-Chevrolet -4141.604895 -5435.638099 -2847.571690 0.0000000
## Mercedes-Benz-Chevrolet 516.685077 -710.645785 1744.015938 0.9999964
## Mini-Chevrolet 4260.568887 1203.846629 7317.291145 0.0000264
## Mitsubishi-Chevrolet -3453.669806 -4825.164191 -2082.175420 0.0000000
## Moskvitch-Chevrolet -7894.219814 -11248.926612 -4539.513015 0.0000000
## Nissan-Chevrolet -2461.617822 -3751.767634 -1171.468010 0.0000000
## Opel-Chevrolet -4447.139096 -5655.383378 -3238.894814 0.0000000
## Peugeot-Chevrolet -4490.914269 -5735.324654 -3246.503884 0.0000000
## Pontiac-Chevrolet -4509.587948 -8297.365541 -721.810355 0.0019651
## Porsche-Chevrolet 9756.691775 6551.836338 12961.547212 0.0000000
## Renault-Chevrolet -4313.908324 -5530.953459 -3096.863189 0.0000000
## Rover-Chevrolet -7138.889783 -9036.130761 -5241.648804 0.0000000
## Saab-Chevrolet -4742.546625 -7262.443336 -2222.649915 0.0000000
## Seat-Chevrolet -5005.116649 -6758.577494 -3251.655804 0.0000000
## Skoda-Chevrolet 4852.941467 3518.536722 6187.346213 0.0000000
## SsangYong-Chevrolet -1153.809590 -4020.529326 1712.910146 0.9999992
## Subaru-Chevrolet -1351.037391 -3125.700324 423.625543 0.6133698
## Suzuki-Chevrolet -3570.533235 -5470.406538 -1670.659932 0.0000000
## Toyota-Chevrolet 925.020460 -379.494734 2229.535655 0.7875086
## UAZ-Chevrolet -5430.008212 -8377.580031 -2482.436393 0.0000000
## Volkswagen-Chevrolet -2445.371175 -3624.455094 -1266.287256 0.0000000
## Volvo-Chevrolet -456.790456 -1879.100296 965.519384 1.0000000
## ZAZ-Chevrolet -7512.920805 -11300.698398 -3725.143212 0.0000000
## Citroen-Chrysler -560.166265 -1861.199361 740.866831 0.9999927
## Dacia-Chrysler 347.259745 -2917.166790 3611.686279 1.0000000
## Daewoo-Chrysler -3418.883047 -5375.316259 -1462.449836 0.0000000
## Dodge-Chrysler 612.520172 -1173.876767 2398.917112 1.0000000
## Fiat-Chrysler -1985.471501 -3402.375604 -568.567399 0.0000222
## Ford-Chrysler 2.185543 -1244.823593 1249.194679 1.0000000
## GAZ-Chrysler -1105.759880 -3127.830893 916.311132 0.9961485
## Geely-Chrysler 2773.740755 -239.888670 5787.370179 0.1445490
## Great Wall-Chrysler 1428.268497 -2647.060388 5503.597383 1.0000000
## Honda-Chrysler 1519.604694 94.748751 2944.460637 0.0178929
## Hyundai-Chrysler 2930.698614 1576.782824 4284.614404 0.0000000
## Infiniti-Chrysler 8799.113158 6623.473378 10974.752937 0.0000000
## Iveco-Chrysler 5056.825342 2755.779579 7357.871104 0.0000000
## Jaguar-Chrysler 12817.508220 9395.358197 16239.658242 0.0000000
## Jeep-Chrysler 5916.997659 3371.926869 8462.068448 0.0000000
## Kia-Chrysler 3160.603746 1766.597267 4554.610225 0.0000000
## LADA-Chrysler 2603.189589 343.715051 4862.664128 0.0040448
## Lancia-Chrysler -2093.983411 -4798.592036 610.625214 0.5665455
## Land Rover-Chrysler 10199.808600 8119.485484 12280.131715 0.0000000
## Lexus-Chrysler 12135.069065 10154.907140 14115.230989 0.0000000
## Lifan-Chrysler 3285.044390 -325.358390 6895.447169 0.1637915
## Lincoln-Chrysler 4741.980442 666.651556 8817.309327 0.0032726
## Mazda-Chrysler -263.964680 -1588.526804 1060.597445 1.0000000
## Mercedes-Benz-Chrysler 4394.325291 3134.847650 5653.802933 0.0000000
## Mini-Chrysler 8138.209102 5068.438125 11207.980079 0.0000000
## Mitsubishi-Chrysler 423.970409 -976.365171 1824.305989 1.0000000
## Moskvitch-Chrysler -4016.579599 -7383.180346 -649.978852 0.0018728
## Nissan-Chrysler 1416.022393 95.253895 2736.790890 0.0163165
## Opel-Chrysler -569.498881 -1810.384505 671.386743 0.9999512
## Peugeot-Chrysler -613.274054 -1889.401043 662.852936 0.9998261
## Pontiac-Chrysler -631.947733 -4430.263410 3166.367944 1.0000000
## Porsche-Chrysler 13634.331990 10417.028576 16851.635404 0.0000000
## Renault-Chrysler -436.268109 -1685.724690 813.188472 1.0000000
## Rover-Chrysler -3261.249568 -5179.443045 -1343.056090 0.0000000
## Saab-Chrysler -864.906410 -3400.615841 1670.803021 1.0000000
## Seat-Chrysler -1127.476434 -2903.586741 648.633873 0.9446190
## Skoda-Chrysler 8730.581682 7366.551290 10094.612074 0.0000000
## SsangYong-Chrysler 2723.830625 -156.798635 5604.459884 0.1055801
## Subaru-Chrysler 2526.602824 729.557635 4323.648014 0.0000199
## Suzuki-Chrysler 307.106980 -1613.690108 2227.904069 1.0000000
## Toyota-Chrysler 4802.660675 3467.856278 6137.465073 0.0000000
## UAZ-Chrysler -1552.367997 -4513.469568 1408.733574 0.9984849
## Volkswagen-Chrysler 1432.269040 219.758515 2644.779566 0.0023479
## Volvo-Chrysler 3420.849759 1970.709019 4870.990500 0.0000000
## ZAZ-Chrysler -3635.280590 -7433.596267 163.035087 0.0908300
## Dacia-Citroen 907.426010 -2201.915767 4016.767787 1.0000000
## Daewoo-Citroen -2858.716783 -4543.696485 -1173.737080 0.0000000
## Dodge-Citroen 1172.686437 -311.476530 2656.849404 0.5089100
## Fiat-Citroen -1425.305237 -2434.831325 -415.779148 0.0000174
## Ford-Citroen 562.351808 -190.348313 1315.051929 0.6637758
## GAZ-Citroen -545.593616 -2306.359605 1215.172373 1.0000000
## Geely-Citroen 3333.907020 489.001398 6178.812641 0.0028018
## Great Wall-Citroen 1988.434762 -1963.758453 5940.627977 0.9994336
## Honda-Citroen 2079.770959 1059.114236 3100.427681 0.0000000
## Hyundai-Citroen 3490.864879 2571.836232 4409.893525 0.0000000
## Infiniti-Citroen 9359.279423 7424.096192 11294.462653 0.0000000
## Iveco-Citroen 5616.991607 3541.819978 7692.163235 0.0000000
## Jaguar-Citroen 13377.674484 10103.130782 16652.218187 0.0000000
## Jeep-Citroen 6477.163924 4134.321417 8820.006430 0.0000000
## Kia-Citroen 3720.770011 2743.641693 4697.898328 0.0000000
## LADA-Citroen 3163.355854 1134.378063 5192.333645 0.0000005
## Lancia-Citroen -1533.817146 -4049.057393 981.423101 0.9702123
## Land Rover-Citroen 10759.974865 8932.607920 12587.341810 0.0000000
## Lexus-Citroen 12695.235329 10982.761408 14407.709251 0.0000000
## Lifan-Citroen 3845.210655 374.398946 7316.022363 0.0089136
## Lincoln-Citroen 5302.146707 1349.953491 9254.339922 0.0000861
## Mazda-Citroen 296.201585 -579.007252 1171.410422 1.0000000
## Mercedes-Benz-Citroen 4954.491556 4181.310066 5727.673047 0.0000000
## Mini-Citroen 8698.375367 5794.064857 11602.685876 0.0000000
## Mitsubishi-Citroen 984.136674 -1.999941 1970.273289 0.0514934
## Moskvitch-Citroen -3456.413334 -6672.859552 -239.967115 0.0156606
## Nissan-Citroen 1976.188658 1106.731867 2845.645448 0.0000000
## Opel-Citroen -9.332616 -751.843773 733.178541 1.0000000
## Peugeot-Citroen -53.107789 -853.123901 746.908323 1.0000000
## Pontiac-Citroen -71.781468 -3737.668491 3594.105555 1.0000000
## Porsche-Citroen 14194.498255 11134.667059 17254.329450 0.0000000
## Renault-Citroen 123.898156 -632.849777 880.646089 1.0000000
## Rover-Citroen -2701.083303 -4341.507609 -1060.658997 0.0000000
## Saab-Citroen -304.740145 -2637.409862 2027.929571 1.0000000
## Seat-Citroen -567.310169 -2039.075608 904.455270 0.9999998
## Skoda-Citroen 9290.747947 8356.882540 10224.613354 0.0000000
## SsangYong-Citroen 3283.996889 580.378829 5987.614950 0.0012298
## Subaru-Citroen 3086.769089 1589.806464 4583.731714 0.0000000
## Suzuki-Citroen 867.273245 -776.194778 2510.741268 0.9982393
## Toyota-Citroen 5362.826940 4472.193196 6253.460685 0.0000000
## UAZ-Citroen -992.201732 -3781.403804 1797.000341 1.0000000
## Volkswagen-Citroen 1992.435305 1298.383822 2686.486788 0.0000000
## Volvo-Citroen 3981.016024 2925.348532 5036.683516 0.0000000
## ZAZ-Citroen -3075.114325 -6741.001348 590.772698 0.3432748
## Daewoo-Dacia -3766.142792 -7201.684974 -330.600611 0.0108902
## Dodge-Dacia 265.260427 -3076.375877 3606.896732 1.0000000
## Fiat-Dacia -2332.731246 -5492.309763 826.847271 0.6942152
## Ford-Dacia -345.074202 -3432.200852 2742.052448 1.0000000
## GAZ-Dacia -1453.019625 -4926.359567 2020.320316 0.9999971
## Geely-Dacia 2426.481010 -1703.565174 6556.527193 0.9842824
## Great Wall-Dacia 1081.008752 -3877.178890 6039.196394 1.0000000
## Honda-Dacia 1172.344949 -1990.807534 4335.497431 1.0000000
## Hyundai-Dacia 2583.438869 -548.398792 5715.276530 0.3880604
## Infiniti-Dacia 8451.853413 4886.923172 12016.783653 0.0000000
## Iveco-Dacia 4709.565597 1066.746528 8352.384666 0.0002481
## Jaguar-Dacia 12470.248475 8033.309221 16907.187729 0.0000000
## Jeep-Dacia 5569.737914 1768.066638 9371.409190 0.0000049
## Kia-Dacia 2813.344001 -336.032641 5962.720643 0.1979198
## LADA-Dacia 2255.929844 -1360.773676 5872.633365 0.9573940
## Lancia-Dacia -2441.243156 -6351.515139 1469.028827 0.9568022
## Land Rover-Dacia 9852.548855 6344.976556 13360.121154 0.0000000
## Lexus-Dacia 11787.809320 8338.699229 15236.919410 0.0000000
## Lifan-Dacia 2937.784645 -1645.917723 7521.487013 0.9364547
## Lincoln-Dacia 4394.720697 -563.466945 9352.908339 0.2136289
## Mazda-Dacia -611.224425 -3730.484592 2508.035743 1.0000000
## Mercedes-Benz-Dacia 4047.065547 954.881351 7139.249742 0.0001763
## Mini-Dacia 7790.949357 3619.760915 11962.137799 0.0000000
## Mitsubishi-Dacia 76.710664 -3075.472532 3228.893861 1.0000000
## Moskvitch-Dacia -4363.839344 -8758.076451 30.397764 0.0552529
## Nissan-Dacia 1068.762648 -2048.888486 4186.413782 1.0000000
## Opel-Dacia -916.758626 -4001.416840 2167.899589 1.0000000
## Peugeot-Dacia -960.533799 -4059.536745 2138.469148 1.0000000
## Pontiac-Dacia -979.207478 -5712.337642 3753.922686 1.0000000
## Porsche-Dacia 13287.072245 9006.142390 17568.002101 0.0000000
## Renault-Dacia -783.527854 -3871.643933 2304.588225 1.0000000
## Rover-Dacia -3608.509313 -7022.419884 -194.598742 0.0208700
## Saab-Dacia -1212.166155 -5007.576737 2583.244427 1.0000000
## Seat-Dacia -1474.736179 -4810.884705 1861.412347 0.9999837
## Skoda-Dacia 8383.321937 5247.098399 11519.545475 0.0000000
## SsangYong-Dacia 2376.570880 -1657.452435 6410.594194 0.9834901
## Subaru-Dacia 2179.343079 -1167.997743 5526.683902 0.9210989
## Suzuki-Dacia -40.152765 -3455.526920 3375.221391 1.0000000
## Toyota-Dacia 4455.400930 1331.777722 7579.024139 0.0000123
## UAZ-Dacia -1899.627742 -5991.502823 2192.247340 0.9999325
## Volkswagen-Dacia 1085.009295 -1988.344076 4158.362666 1.0000000
## Volvo-Dacia 3073.590014 -101.032379 6248.212407 0.0782541
## ZAZ-Dacia -3982.540335 -8715.670499 750.589829 0.3353173
## Dodge-Daewoo 4031.403220 1948.692605 6114.113835 0.0000000
## Fiat-Daewoo 1433.411546 -342.563317 3209.386410 0.4485735
## Ford-Daewoo 3421.068590 1777.444161 5064.693020 0.0000000
## GAZ-Daewoo 2313.123167 25.058338 4601.187996 0.0425667
## Geely-Daewoo 6192.623802 2994.431846 9390.815758 0.0000000
## Great Wall-Daewoo 4847.151545 633.510007 9060.793082 0.0041786
## Honda-Daewoo 4938.487741 3156.162311 6720.813171 0.0000000
## Hyundai-Daewoo 6349.581661 4623.442198 8075.721125 0.0000000
## Infiniti-Daewoo 12217.996205 9793.150245 14642.842165 0.0000000
## Iveco-Daewoo 8475.708389 5937.740485 11013.676293 0.0000000
## Jaguar-Daewoo 16236.391267 12650.644447 19822.138087 0.0000000
## Jeep-Daewoo 9335.880706 6574.747822 12097.013590 0.0000000
## Kia-Daewoo 6579.486793 4821.725847 8337.247739 0.0000000
## LADA-Daewoo 6022.072637 3521.733726 8522.411548 0.0000000
## Lancia-Daewoo 1324.899637 -1583.945030 4233.744303 0.9999608
## Land Rover-Daewoo 13618.691647 11278.987875 15958.395420 0.0000000
## Lexus-Daewoo 15553.952112 13302.838893 17805.065331 0.0000000
## Lifan-Daewoo 6703.927437 2938.097156 10469.757719 0.0000000
## Lincoln-Daewoo 8160.863489 3947.221952 12374.505026 0.0000000
## Mazda-Daewoo 3154.918368 1451.705433 4858.131303 0.0000000
## Mercedes-Benz-Daewoo 7813.208339 6160.104165 9466.312513 0.0000000
## Mini-Daewoo 11557.092149 8305.944148 14808.240151 0.0000000
## Mitsubishi-Daewoo 3842.853457 2080.068950 5605.637963 0.0000000
## Moskvitch-Daewoo -597.696551 -4130.467428 2935.074326 1.0000000
## Nissan-Daewoo 4834.905440 3134.641077 6535.169803 0.0000000
## Opel-Daewoo 2849.384167 1210.400759 4488.367575 0.0000000
## Peugeot-Daewoo 2805.608994 1139.785001 4471.432986 0.0000000
## Pontiac-Daewoo 2786.935315 -1159.413816 6733.284445 0.7959176
## Porsche-Daewoo 17053.215037 13662.417087 20444.012988 0.0000000
## Renault-Daewoo 2982.614939 1337.132873 4628.097004 0.0000000
## Rover-Daewoo 157.633480 -2039.167763 2354.434722 1.0000000
## Saab-Daewoo 2553.976637 -198.529820 5306.483095 0.1319617
## Seat-Daewoo 2291.406613 217.512378 4365.300849 0.0093870
## Skoda-Daewoo 12149.464730 10415.380420 13883.549039 0.0000000
## SsangYong-Daewoo 6142.713672 3069.524057 9215.903287 0.0000000
## Subaru-Daewoo 5945.485872 3853.634803 8037.336940 0.0000000
## Suzuki-Daewoo 3725.990028 1526.915010 5925.065046 0.0000000
## Toyota-Daewoo 8221.543723 6510.353418 9932.734028 0.0000000
## UAZ-Daewoo 1866.515051 -1282.229460 5015.259562 0.9815787
## Volkswagen-Daewoo 4851.152088 3233.545444 6468.758732 0.0000000
## Volvo-Daewoo 6839.732806 5037.129787 8642.335826 0.0000000
## ZAZ-Daewoo -216.397543 -4162.746673 3729.951588 1.0000000
## Fiat-Dodge -2597.991674 -4184.708356 -1011.274991 0.0000001
## Ford-Dodge -610.334629 -2047.374775 826.705516 0.9999952
## GAZ-Dodge -1718.280053 -3862.766886 426.206780 0.4691695
## Geely-Dodge 2161.220582 -935.877237 5258.318402 0.8193913
## Great Wall-Dodge 815.748325 -3321.685435 4953.182085 1.0000000
## Honda-Dodge 907.084521 -686.737003 2500.906046 0.9913327
## Hyundai-Dodge 2318.178441 787.446407 3848.910476 0.0000014
## Infiniti-Dodge 8186.592985 5896.732499 10476.453471 0.0000000
## Iveco-Dodge 4444.305169 2034.976592 6853.633747 0.0000000
## Jaguar-Dodge 12204.988047 8709.109993 15700.866101 0.0000000
## Jeep-Dodge 5304.477486 2661.101252 7947.853721 0.0000000
## Kia-Dodge 2548.083573 981.780072 4114.387075 0.0000001
## LADA-Dodge 1990.669417 -378.988319 4360.327153 0.3394870
## Lancia-Dodge -2706.503583 -5503.816653 90.809486 0.0789356
## Land Rover-Dodge 9587.288428 7387.789198 11786.787657 0.0000000
## Lexus-Dodge 11522.548892 9417.532540 13627.565244 0.0000000
## Lifan-Dodge 2672.524217 -1007.837290 6352.885725 0.7346379
## Lincoln-Dodge 4129.460269 -7.973491 8266.894030 0.0514182
## Mazda-Dodge -876.484852 -2381.316210 628.346506 0.9865763
## Mercedes-Benz-Dodge 3781.805119 2333.932015 5229.678223 0.0000000
## Mini-Dodge 7525.688930 4373.936013 10677.441846 0.0000000
## Mitsubishi-Dodge -188.549763 -1760.488799 1383.389272 1.0000000
## Moskvitch-Dodge -4629.099771 -8070.618794 -1187.580748 0.0000797
## Nissan-Dodge 803.502221 -697.991043 2304.995484 0.9975905
## Opel-Dodge -1182.019053 -2613.748681 249.710574 0.3857758
## Peugeot-Dodge -1225.794226 -2688.173346 236.584893 0.3452286
## Pontiac-Dodge -1244.467905 -5109.342363 2620.406553 1.0000000
## Porsche-Dodge 13021.811818 9726.194447 16317.429188 0.0000000
## Renault-Dodge -1048.788281 -2487.952741 390.376179 0.7261919
## Rover-Dodge -3873.769740 -5920.601148 -1826.938332 0.0000000
## Saab-Dodge -1477.426582 -4111.790816 1156.937651 0.9935149
## Seat-Dodge -1739.996606 -3654.316438 174.323225 0.1656122
## Skoda-Dodge 8118.061510 6578.375988 9657.747032 0.0000000
## SsangYong-Dodge 2111.310452 -856.530097 5079.151002 0.7806750
## Subaru-Dodge 1914.082652 -19.676384 3847.841688 0.0578865
## Suzuki-Dodge -305.413192 -2354.684782 1743.858398 1.0000000
## Toyota-Dodge 4190.140503 2676.286027 5703.994979 0.0000000
## UAZ-Dodge -2164.888169 -5210.897872 881.121534 0.7826629
## Volkswagen-Dodge 819.748868 -587.459137 2226.956873 0.9865388
## Volvo-Dodge 2808.329587 1191.864071 4424.795102 0.0000000
## ZAZ-Dodge -4247.800762 -8112.675220 -382.926304 0.0103724
## Ford-Fiat 1987.657044 1048.781338 2926.532750 0.0000000
## GAZ-Fiat 879.711621 -968.321654 2727.744896 0.9998653
## Geely-Fiat 4759.212256 1859.485008 7658.939504 0.0000001
## Great Wall-Fiat 3413.739999 -578.096838 7405.576835 0.2944698
## Honda-Fiat 3505.076195 2340.314304 4669.838087 0.0000000
## Hyundai-Fiat 4916.170115 3839.348973 5992.991258 0.0000000
## Infiniti-Fiat 10784.584659 8769.674176 12799.495143 0.0000000
## Iveco-Fiat 7042.296843 4892.583512 9192.010174 0.0000000
## Jaguar-Fiat 14802.979721 11480.696383 18125.263059 0.0000000
## Jeep-Fiat 7902.469160 5493.352905 10311.585415 0.0000000
## Kia-Fiat 5146.075247 4019.261213 6272.889282 0.0000000
## LADA-Fiat 4588.661091 2483.505278 6693.816903 0.0000000
## Lancia-Fiat -108.511910 -2685.596235 2468.572416 1.0000000
## Land Rover-Fiat 12185.280101 10273.683891 14096.876311 0.0000000
## Lexus-Fiat 14120.540566 12318.459255 15922.621877 0.0000000
## Lifan-Fiat 5270.515891 1754.628516 8786.403266 0.0000021
## Lincoln-Fiat 6727.451943 2735.615106 10719.288780 0.0000000
## Mazda-Fiat 1721.506822 681.833495 2761.180149 0.0000000
## Mercedes-Benz-Fiat 6379.796793 5424.422659 7335.170927 0.0000000
## Mini-Fiat 10123.680603 7165.649222 13081.711985 0.0000000
## Mitsubishi-Fiat 2409.441910 1274.807369 3544.076452 0.0000000
## Moskvitch-Fiat -2031.108097 -5296.143533 1233.927338 0.9591205
## Nissan-Fiat 3401.493894 2366.658047 4436.329741 0.0000000
## Opel-Fiat 1415.972620 485.245519 2346.699722 0.0000012
## Peugeot-Fiat 1372.197447 394.978997 2349.415897 0.0000208
## Pontiac-Fiat 1353.523768 -2355.068658 5062.116195 1.0000000
## Porsche-Fiat 15619.803491 12508.935917 18730.671066 0.0000000
## Renault-Fiat 1549.203393 607.079434 2491.327351 0.0000000
## Rover-Fiat -1275.778066 -3009.537572 457.981439 0.7025596
## Saab-Fiat 1120.565091 -1278.659393 3519.789575 0.9999201
## Seat-Fiat 857.995067 -717.131476 2433.121611 0.9964555
## Skoda-Fiat 10716.053184 9626.541955 11805.564412 0.0000000
## SsangYong-Fiat 4709.302126 1948.055897 7470.548355 0.0000000
## Subaru-Fiat 4512.074326 2913.378852 6110.769800 0.0000000
## Suzuki-Fiat 2292.578482 555.938835 4029.218128 0.0001379
## Toyota-Fiat 6788.132177 5735.441061 7840.823293 0.0000000
## UAZ-Fiat 433.103505 -2411.993738 3278.200747 1.0000000
## Volkswagen-Fiat 3417.740541 2525.194979 4310.286103 0.0000000
## Volvo-Fiat 5406.321260 4210.761152 6601.881369 0.0000000
## ZAZ-Fiat -1649.809089 -5358.401515 2058.783338 0.9999803
## GAZ-Ford -1107.945423 -2829.177986 613.287140 0.9324864
## Geely-Ford 2771.555212 -49.053351 5592.163774 0.0641047
## Great Wall-Ford 1426.082954 -2508.656666 5360.822575 1.0000000
## Honda-Ford 1517.419151 566.585404 2468.252897 0.0000002
## Hyundai-Ford 2928.513071 2087.704934 3769.321207 0.0000000
## Infiniti-Ford 8796.927615 6897.643854 10696.211376 0.0000000
## Iveco-Ford 5054.639799 3012.904763 7096.374835 0.0000000
## Jaguar-Ford 12815.322677 9561.865880 16068.779474 0.0000000
## Jeep-Ford 5914.812116 3601.533992 8228.090240 0.0000000
## Kia-Ford 3158.418203 2254.468858 4062.367548 0.0000000
## LADA-Ford 2601.004046 606.237006 4595.771086 0.0001958
## Lancia-Ford -2096.168954 -4583.894455 391.556547 0.3316789
## Land Rover-Ford 10197.623057 8408.317432 11986.928682 0.0000000
## Lexus-Ford 12132.883522 10461.084610 13804.682433 0.0000000
## Lifan-Ford 3282.858847 -168.065462 6733.783155 0.0980099
## Lincoln-Ford 4739.794899 805.055278 8674.534519 0.0014979
## Mazda-Ford -266.150223 -1058.826182 526.525737 1.0000000
## Mercedes-Benz-Ford 4392.139749 3713.793019 5070.486478 0.0000000
## Mini-Ford 8136.023559 5255.508986 11016.538132 0.0000000
## Mitsubishi-Ford 421.784866 -491.894559 1335.464291 0.9999424
## Moskvitch-Ford -4018.765142 -7213.741033 -823.789250 0.0005156
## Nissan-Ford 1413.836850 627.516445 2200.157255 0.0000000
## Opel-Ford -571.684424 -1214.854298 71.485450 0.2078815
## Peugeot-Ford -615.459597 -1324.240556 93.321363 0.2583181
## Pontiac-Ford -634.133276 -4281.196798 3012.930246 1.0000000
## Porsche-Ford 13632.146447 10594.892488 16669.400406 0.0000000
## Renault-Ford -438.453652 -1098.008125 221.100821 0.8965993
## Rover-Ford -3263.435111 -4861.351433 -1665.518788 0.0000000
## Saab-Ford -867.091953 -3170.066698 1435.882791 0.9999999
## Seat-Ford -1129.661977 -2553.894464 294.570510 0.4979819
## Skoda-Ford 8728.396139 7871.395984 9585.396295 0.0000000
## SsangYong-Ford 2721.645082 43.605633 5399.684530 0.0393110
## Subaru-Ford 2524.417281 1074.161522 3974.673041 0.0000000
## Suzuki-Ford 304.921437 -1296.119415 1905.962289 1.0000000
## Toyota-Ford 4800.475132 3990.800423 5610.149842 0.0000000
## UAZ-Ford -1554.553540 -4318.969006 1209.861926 0.9931549
## Volkswagen-Ford 1430.083497 843.523927 2016.643068 0.0000000
## Volvo-Ford 3418.664216 2430.343154 4406.985279 0.0000000
## ZAZ-Ford -3637.466133 -7284.529655 9.597389 0.0519451
## Geely-GAZ 3879.500635 640.739752 7118.261519 0.0017082
## Great Wall-GAZ 2534.028378 -1710.487528 6778.544284 0.9791452
## Honda-GAZ 2625.364574 771.227522 4479.501627 0.0000158
## Hyundai-GAZ 4036.458494 2236.264694 5836.652294 0.0000000
## Infiniti-GAZ 9904.873038 7426.765252 12382.980825 0.0000000
## Iveco-GAZ 6162.585222 3573.681719 8751.488726 0.0000000
## Jaguar-GAZ 13923.268100 10301.290667 17545.245533 0.0000000
## Jeep-GAZ 7022.757539 4214.734200 9830.780879 0.0000000
## Kia-GAZ 4266.363626 2435.827144 6096.900109 0.0000000
## LADA-GAZ 3708.949470 1156.923816 6260.975124 0.0000065
## Lancia-GAZ -988.223530 -3941.614389 1965.167328 1.0000000
## Land Rover-GAZ 11305.568480 8910.708565 13700.428396 0.0000000
## Lexus-GAZ 13240.828945 10932.441846 15549.216044 0.0000000
## Lifan-GAZ 4390.804270 590.459815 8191.148726 0.0038102
## Lincoln-GAZ 5847.740322 1603.224416 10092.256228 0.0000382
## Mazda-GAZ 841.795201 -936.427106 2620.017508 0.9998842
## Mercedes-Benz-GAZ 5500.085172 3769.798005 7230.372339 0.0000000
## Mini-GAZ 9243.968982 5952.904778 12535.033187 0.0000000
## Mitsubishi-GAZ 1529.730290 -305.630571 3365.091150 0.3601966
## Moskvitch-GAZ -2910.819718 -6480.358952 658.719516 0.4197465
## Nissan-GAZ 2521.782273 746.383959 4297.180588 0.0000142
## Opel-GAZ 536.261000 -1180.540352 2253.062352 1.0000000
## Peugeot-GAZ 492.485827 -1249.957815 2234.929468 1.0000000
## Pontiac-GAZ 473.812148 -3505.485747 4453.110042 1.0000000
## Porsche-GAZ 14740.091870 11311.002923 18169.180818 0.0000000
## Renault-GAZ 669.491772 -1053.514757 2392.498301 0.9999997
## Rover-GAZ -2155.489687 -4410.944402 99.965027 0.0925267
## Saab-GAZ 240.853470 -2558.687933 3040.394874 1.0000000
## Seat-GAZ -21.716553 -2157.642014 2114.208907 1.0000000
## Skoda-GAZ 9836.341563 8028.528336 11644.154789 0.0000000
## SsangYong-GAZ 3829.590505 714.203742 6944.977268 0.0009207
## Subaru-GAZ 3632.362705 1478.997626 5785.727784 0.0000000
## Suzuki-GAZ 1412.866861 -844.802558 3670.536280 0.9552023
## Toyota-GAZ 5908.420556 4122.555911 7694.285201 0.0000000
## UAZ-GAZ -446.608116 -3636.550477 2743.334245 1.0000000
## Volkswagen-GAZ 2538.028921 841.623449 4234.434392 0.0000023
## Volvo-GAZ 4526.609640 2652.972023 6400.247256 0.0000000
## ZAZ-GAZ -2529.520710 -6508.818604 1449.777185 0.9435079
## Great Wall-Geely -1345.472257 -6142.253095 3451.308580 1.0000000
## Honda-Geely -1254.136061 -4157.757134 1649.485012 0.9999920
## Hyundai-Geely 156.957859 -2712.517502 3026.433220 1.0000000
## Infiniti-Geely 6025.372403 2688.576136 9362.168670 0.0000000
## Iveco-Geely 2283.084587 -1136.800276 5702.969451 0.8910122
## Jaguar-Geely 10043.767465 5787.958299 14299.576630 0.0000000
## Jeep-Geely 3143.256904 -445.362079 6731.875887 0.2388796
## Kia-Geely 386.862991 -2501.744795 3275.470777 1.0000000
## LADA-Geely -170.551165 -3562.604536 3221.502205 1.0000000
## Lancia-Geely -4867.724166 -8571.196684 -1164.251647 0.0001563
## Land Rover-Geely 7426.067845 4150.622052 10701.513638 0.0000000
## Lexus-Geely 9361.328310 6148.565928 12574.090692 0.0000000
## Lifan-Geely 511.303635 -3897.302620 4919.909890 1.0000000
## Lincoln-Geely 1968.239687 -2828.541151 6765.020525 0.9999984
## Mazda-Geely -3037.705434 -5893.448017 -181.962851 0.0187217
## Mercedes-Benz-Geely 1620.584537 -1205.558561 4446.727635 0.9899806
## Mini-Geely 5364.468347 1386.492137 9342.444557 0.0000738
## Mitsubishi-Geely -2349.770346 -5241.437791 541.897100 0.4295471
## Moskvitch-Geely -6790.320353 -11001.591120 -2579.049587 0.0000001
## Nissan-Geely -1357.718362 -4211.703348 1496.266625 0.9998676
## Opel-Geely -3343.239636 -6161.146307 -525.332964 0.0021271
## Peugeot-Geely -3387.014809 -6220.616928 -553.412689 0.0017938
## Pontiac-Geely -3405.688488 -7969.459172 1158.082197 0.6668115
## Porsche-Geely 10860.591235 6767.689803 14953.492668 0.0000000
## Renault-Geely -3210.008863 -6031.700311 -388.317416 0.0052663
## Rover-Geely -6034.990322 -9209.933959 -2860.046686 0.0000000
## Saab-Geely -3638.647165 -7220.633094 -56.661236 0.0395856
## Seat-Geely -3901.217189 -6992.393130 -810.041247 0.0004727
## Skoda-Geely 5956.840927 3082.579320 8831.102535 0.0000000
## SsangYong-Geely -49.910130 -3883.814903 3783.994643 1.0000000
## Subaru-Geely -247.137930 -3350.389820 2856.113959 1.0000000
## Suzuki-Geely -2466.633775 -5643.151102 709.883553 0.5582141
## Toyota-Geely 2028.919921 -831.587667 4889.427508 0.7869335
## UAZ-Geely -4326.108751 -8220.838959 -431.378543 0.0084772
## Volkswagen-Geely -1341.471715 -4146.998876 1464.055447 0.9998481
## Volvo-Geely 647.109004 -2269.002970 3563.220978 1.0000000
## ZAZ-Geely -6409.021345 -10972.792029 -1845.250660 0.0000207
## Honda-Great Wall 91.336197 -3903.330066 4086.002459 1.0000000
## Hyundai-Great Wall 1502.430116 -2467.485759 5472.345992 0.9999999
## Infiniti-Great Wall 7370.844660 3051.058492 11690.630829 0.0000000
## Iveco-Great Wall 3628.556845 -755.728239 8012.841928 0.3790832
## Jaguar-Great Wall 11389.239722 6325.816486 16452.662958 0.0000000
## Jeep-Great Wall 4488.729161 -28.408142 9005.866465 0.0547567
## Kia-Great Wall 1732.335249 -2251.431582 5716.102079 0.9999901
## LADA-Great Wall 1174.921092 -3187.689283 5537.531467 1.0000000
## Lancia-Great Wall -3522.251908 -8131.161965 1086.658148 0.6027919
## Land Rover-Great Wall 8771.540103 4498.966129 13044.114076 0.0000000
## Lexus-Great Wall 10706.800567 6482.089294 14931.511840 0.0000000
## Lifan-Great Wall 1856.775892 -3335.733359 7049.285144 1.0000000
## Lincoln-Great Wall 3313.711944 -2212.177090 8839.600979 0.9774907
## Mazda-Great Wall -1692.233177 -5652.234303 2267.767949 0.9999942
## Mercedes-Benz-Great Wall 2966.056794 -972.652135 6904.765723 0.6428317
## Mini-Great Wall 6709.940605 1877.690824 11542.190385 0.0000298
## Mitsubishi-Great Wall -1004.298088 -4990.284018 2981.687841 1.0000000
## Moskvitch-Great Wall -5444.848096 -10470.894722 -418.801470 0.0135452
## Nissan-Great Wall -12.246104 -3970.979932 3946.487723 1.0000000
## Opel-Great Wall -1997.767378 -5930.570606 1935.035849 0.9992828
## Peugeot-Great Wall -2041.542551 -5985.606976 1902.521874 0.9988738
## Pontiac-Great Wall -2060.216230 -7385.096142 3264.663682 0.9999998
## Porsche-Great Wall 12206.063493 7278.773634 17133.353352 0.0000000
## Renault-Great Wall -1864.536606 -5800.052562 2070.979350 0.9998816
## Rover-Great Wall -4689.518065 -8885.541219 -493.494911 0.0075199
## Saab-Great Wall -2293.174907 -6805.044410 2218.694596 0.9992732
## Seat-Great Wall -2555.744931 -6688.747704 1577.257841 0.9627519
## Skoda-Great Wall 7302.313185 3328.936409 11275.689961 0.0000000
## SsangYong-Great Wall 1295.562127 -3418.795626 6009.919881 1.0000000
## Subaru-Great Wall 1098.334327 -3043.708105 5240.376759 1.0000000
## Suzuki-Great Wall -1121.161517 -5318.375539 3076.052505 1.0000000
## Toyota-Great Wall 3374.392178 -589.046591 7337.830947 0.3054490
## UAZ-Great Wall -2980.636494 -7744.591428 1783.318440 0.9553579
## Volkswagen-Great Wall 4.000543 -3919.942101 3927.943186 1.0000000
## Volvo-Great Wall 1992.581262 -2011.173509 5996.336032 0.9995686
## ZAZ-Great Wall -5063.549087 -10388.428999 261.330824 0.0984923
## Hyundai-Honda 1411.093920 323.830845 2498.356995 0.0002230
## Infiniti-Honda 7279.508464 5258.998261 9300.018666 0.0000000
## Iveco-Honda 3537.220648 1382.257857 5692.183439 0.0000001
## Jaguar-Honda 11297.903526 7972.221069 14623.585982 0.0000000
## Jeep-Honda 4397.392965 1983.591326 6811.194604 0.0000000
## Kia-Honda 1640.999052 504.202197 2777.795908 0.0000082
## LADA-Honda 1083.584896 -1026.931206 3194.100998 0.9990721
## Lancia-Honda -3613.588105 -6195.052968 -1032.123241 0.0000230
## Land Rover-Honda 8680.203906 6762.706242 10597.701570 0.0000000
## Lexus-Honda 10615.464371 8807.124172 12423.804569 0.0000000
## Lifan-Honda 1765.439696 -1753.659799 5284.539191 0.9994728
## Lincoln-Honda 3222.375748 -772.290515 7217.042011 0.4501210
## Mazda-Honda -1783.569373 -2834.053952 -733.084795 0.0000000
## Mercedes-Benz-Honda 2874.720598 1907.592396 3841.848799 0.0000000
## Mini-Honda 6618.604408 3656.755851 9580.452966 0.0000000
## Mitsubishi-Honda -1095.634285 -2240.183438 48.914869 0.0906066
## Moskvitch-Honda -5536.184292 -8804.678383 -2267.690202 0.0000000
## Nissan-Honda -103.582301 -1149.279414 942.114812 1.0000000
## Opel-Honda -2089.103575 -3031.892077 -1146.315073 0.0000000
## Peugeot-Honda -2132.878748 -3121.591609 -1144.165887 0.0000000
## Pontiac-Honda -2151.552427 -5863.190206 1560.085353 0.9877092
## Porsche-Honda 12114.727296 9000.229859 15229.224733 0.0000000
## Renault-Honda -1955.872803 -2909.914089 -1001.831516 0.0000000
## Rover-Honda -4780.854262 -6521.118393 -3040.590130 0.0000000
## Saab-Honda -2384.511104 -4788.440251 19.418044 0.0561787
## Seat-Honda -2647.081128 -4229.364557 -1064.797699 0.0000000
## Skoda-Honda 7210.976988 6111.144302 8310.809675 0.0000000
## SsangYong-Honda 1204.225931 -1561.109123 3969.560985 0.9999897
## Subaru-Honda 1006.998131 -598.749185 2612.745446 0.9537581
## Suzuki-Honda -1212.497714 -2955.631238 530.635811 0.8254003
## Toyota-Honda 3283.055982 2219.685958 4346.426006 0.0000000
## UAZ-Honda -3071.972690 -5921.038422 -222.906959 0.0147454
## Volkswagen-Honda -87.335654 -992.451559 817.780252 1.0000000
## Volvo-Honda 1901.245065 696.271541 3106.218590 0.0000003
## ZAZ-Honda -5154.885284 -8866.523064 -1443.247504 0.0000296
## Infiniti-Hyundai 5868.414544 3897.289234 7839.539854 0.0000000
## Iveco-Hyundai 2126.126728 17.397694 4234.855762 0.0443046
## Jaguar-Hyundai 9886.809606 6590.897393 13182.721818 0.0000000
## Jeep-Hyundai 2986.299045 613.681954 5358.916136 0.0005071
## Kia-Hyundai 229.905132 -816.603635 1276.413899 1.0000000
## LADA-Hyundai -327.509024 -2390.795656 1735.777608 1.0000000
## Lancia-Hyundai -5024.682025 -7567.679145 -2481.684905 0.0000000
## Land Rover-Hyundai 7269.109986 5403.722412 9134.497560 0.0000000
## Lexus-Hyundai 9204.370451 7451.382207 10957.358695 0.0000000
## Lifan-Hyundai 354.345776 -3136.633280 3845.324831 1.0000000
## Lincoln-Hyundai 1811.281828 -2158.634048 5781.197704 0.9999588
## Mazda-Hyundai -3194.663293 -4146.709174 -2242.617413 0.0000000
## Mercedes-Benz-Hyundai 1463.626678 604.434925 2322.818430 0.0000000
## Mini-Hyundai 5207.510488 2279.128616 8135.892360 0.0000000
## Mitsubishi-Hyundai -2506.728205 -3561.652976 -1451.803433 0.0000000
## Moskvitch-Hyundai -6947.278212 -10185.476342 -3709.080083 0.0000000
## Nissan-Hyundai -1514.676221 -2461.436994 -567.915448 0.0000002
## Opel-Hyundai -3500.197495 -4331.896760 -2668.498229 0.0000000
## Peugeot-Hyundai -3543.972668 -4427.390253 -2660.555082 0.0000000
## Pontiac-Hyundai -3562.646347 -7247.633255 122.340562 0.0797285
## Porsche-Hyundai 10703.633376 7620.944972 13786.321780 0.0000000
## Renault-Hyundai -3366.966723 -4211.400429 -2522.533016 0.0000000
## Rover-Hyundai -6191.948181 -7874.622468 -4509.273895 0.0000000
## Saab-Hyundai -3795.605024 -6158.177532 -1433.032516 0.0000001
## Seat-Hyundai -4058.175048 -5576.889754 -2539.460342 0.0000000
## Skoda-Hyundai 5799.883068 4793.649771 6806.116365 0.0000000
## SsangYong-Hyundai -206.867989 -2936.327912 2522.591933 1.0000000
## Subaru-Hyundai -404.095789 -1947.241262 1139.049683 1.0000000
## Suzuki-Hyundai -2623.591634 -4309.233349 -937.949918 0.0000005
## Toyota-Hyundai 1871.962062 905.717105 2838.207019 0.0000000
## UAZ-Hyundai -4483.066610 -7297.324781 -1668.808440 0.0000002
## Volkswagen-Hyundai -1498.429574 -2287.168004 -709.691143 0.0000000
## Volvo-Hyundai 490.151145 -630.042902 1610.345193 0.9999880
## ZAZ-Hyundai -6565.979204 -10250.966112 -2880.992295 0.0000000
## Iveco-Infiniti -3742.287816 -6452.833632 -1031.742000 0.0000357
## Jaguar-Infiniti 4018.395062 308.495091 7728.295033 0.0135842
## Jeep-Infiniti -2882.115499 -5802.668930 38.437932 0.0603942
## Kia-Infiniti -5638.509412 -7637.384366 -3639.634458 0.0000000
## LADA-Infiniti -6195.923568 -8871.268819 -3520.578318 0.0000000
## Lancia-Infiniti -10893.096569 -13953.677369 -7832.515768 0.0000000
## Land Rover-Infiniti 1400.695442 -1125.169021 3926.559905 0.9948431
## Lexus-Infiniti 3335.955907 891.924756 5779.987058 0.0000511
## Lifan-Infiniti -5514.068768 -9398.300384 -1629.837152 0.0000145
## Lincoln-Infiniti -4057.132716 -8376.918885 262.653453 0.1145421
## Mazda-Infiniti -9063.077837 -11014.157498 -7111.998177 0.0000000
## Mercedes-Benz-Infiniti -4404.787866 -6312.281235 -2497.294498 0.0000000
## Mini-Infiniti -660.904056 -4048.490346 2726.682234 1.0000000
## Mitsubishi-Infiniti -8375.142749 -10378.436725 -6371.848772 0.0000000
## Moskvitch-Infiniti -12815.692756 -16474.414885 -9156.970628 0.0000000
## Nissan-Infiniti -7383.090765 -9331.596973 -5434.584556 0.0000000
## Opel-Infiniti -9368.612039 -11263.880925 -7473.343152 0.0000000
## Peugeot-Infiniti -9412.387212 -11330.914541 -7493.859882 0.0000000
## Pontiac-Infiniti -9431.060891 -13490.549652 -5371.572130 0.0000000
## Porsche-Infiniti 4835.218832 1313.388587 8357.049078 0.0000426
## Renault-Infiniti -9235.381267 -11136.272838 -7334.489695 0.0000000
## Rover-Infiniti -12060.362726 -14454.462397 -9666.263054 0.0000000
## Saab-Infiniti -9664.019568 -12576.418809 -6751.620327 0.0000000
## Seat-Infiniti -9926.589592 -12208.434206 -7644.744978 0.0000000
## Skoda-Infiniti -68.531476 -2046.617907 1909.554956 1.0000000
## SsangYong-Infiniti -6075.282533 -9292.466388 -2858.098679 0.0000000
## Subaru-Infiniti -6272.510333 -8570.687531 -3974.333135 0.0000000
## Suzuki-Infiniti -8492.006178 -10888.192413 -6095.819942 0.0000000
## Toyota-Infiniti -3996.452482 -5954.499928 -2038.405036 0.0000000
## UAZ-Infiniti -10351.481154 -13640.914166 -7062.048143 0.0000000
## Volkswagen-Infiniti -7366.844118 -9243.657586 -5490.030649 0.0000000
## Volvo-Infiniti -5378.263399 -7416.683176 -3339.843622 0.0000000
## ZAZ-Infiniti -12434.393748 -16493.882509 -8374.904987 0.0000000
## Jaguar-Iveco 7760.682878 3975.876285 11545.489471 0.0000000
## Jeep-Iveco 860.172317 -2154.962090 3875.306724 1.0000000
## Kia-Iveco -1896.221596 -4030.912261 238.469069 0.2091736
## LADA-Iveco -2453.635752 -5231.922159 324.650655 0.2211719
## Lancia-Iveco -7150.808753 -10301.770191 -3999.847314 0.0000000
## Land Rover-Iveco 5142.983258 2508.330637 7777.635880 0.0000000
## Lexus-Iveco 7078.243723 4521.939473 9634.547972 0.0000000
## Lifan-Iveco -1771.780952 -5727.619473 2184.057568 0.9999760
## Lincoln-Iveco -314.844900 -4699.129984 4069.440184 1.0000000
## Mazda-Iveco -5320.790021 -7410.793608 -3230.786435 0.0000000
## Mercedes-Benz-Iveco -662.500050 -2711.874126 1386.874025 1.0000000
## Mini-Iveco 3081.383760 -388.074990 6550.842510 0.2095018
## Mitsubishi-Iveco -4632.854933 -6771.684030 -2494.025835 0.0000000
## Moskvitch-Iveco -9073.404940 -12808.060314 -5338.749567 0.0000000
## Nissan-Iveco -3640.802949 -5728.404346 -1553.201551 0.0000000
## Opel-Iveco -5626.324223 -7664.325033 -3588.323412 0.0000000
## Peugeot-Iveco -5670.099396 -7729.747488 -3610.451303 0.0000000
## Pontiac-Iveco -5688.773075 -9816.829779 -1560.716371 0.0000379
## Porsche-Iveco 8577.506648 4976.855120 12178.158177 0.0000000
## Renault-Iveco -5493.093451 -7536.324206 -3449.862695 0.0000000
## Rover-Iveco -8318.074910 -10826.683366 -5809.466453 0.0000000
## Saab-Iveco -5921.731752 -8928.968439 -2914.495065 0.0000000
## Seat-Iveco -6184.301776 -8586.013247 -3782.590304 0.0000000
## Skoda-Iveco 3673.756340 1558.518982 5788.993699 0.0000000
## SsangYong-Iveco -2332.994717 -5636.277191 970.287757 0.7957455
## Subaru-Iveco -2530.222517 -4947.456802 -112.988233 0.0245814
## Suzuki-Iveco -4749.718362 -7260.318215 -2239.118508 0.0000000
## Toyota-Iveco -254.164666 -2350.674373 1842.345040 1.0000000
## UAZ-Iveco -6609.193339 -9982.881630 -3235.505047 0.0000000
## Volkswagen-Iveco -3624.556302 -5645.405615 -1603.706989 0.0000000
## Volvo-Iveco -1635.975583 -3807.739460 535.788294 0.6419781
## ZAZ-Iveco -8692.105932 -12820.162636 -4564.049228 0.0000000
## Jeep-Jaguar -6900.510561 -10838.445891 -2962.575230 0.0000000
## Kia-Jaguar -9656.904474 -12969.487062 -6344.321885 0.0000000
## LADA-Jaguar -10214.318630 -13973.996081 -6454.641180 0.0000000
## Lancia-Jaguar -14911.491630 -18954.368967 -10868.614294 0.0000000
## Land Rover-Jaguar -2617.699620 -6272.517482 1037.118243 0.7660110
## Lexus-Jaguar -682.439155 -4281.187631 2916.309321 1.0000000
## Lifan-Jaguar -9532.463830 -14229.798986 -4835.128673 0.0000000
## Lincoln-Jaguar -8075.527778 -13138.951014 -3012.104542 0.0000002
## Mazda-Jaguar -13081.472899 -16365.436079 -9797.509719 0.0000000
## Mercedes-Benz-Jaguar -8423.182928 -11681.439099 -5164.926757 0.0000000
## Mini-Jaguar -4679.299118 -8975.046223 -383.552012 0.0122529
## Mitsubishi-Jaguar -12393.537810 -15708.788793 -9078.286827 0.0000000
## Moskvitch-Jaguar -16834.087818 -21346.731160 -12321.444476 0.0000000
## Nissan-Jaguar -11401.485827 -14683.920711 -8119.050942 0.0000000
## Opel-Jaguar -13387.007100 -16638.121752 -10135.892449 0.0000000
## Peugeot-Jaguar -13430.782273 -16695.510354 -10166.054193 0.0000000
## Pontiac-Jaguar -13449.455952 -18292.714290 -8606.197615 0.0000000
## Porsche-Jaguar 816.823770 -3585.560879 5219.208420 1.0000000
## Renault-Jaguar -13253.776328 -16508.171985 -9999.380671 0.0000000
## Rover-Jaguar -16078.757787 -19643.784514 -12513.731061 0.0000000
## Saab-Jaguar -13682.414630 -17614.306243 -9750.523016 0.0000000
## Seat-Jaguar -13944.984653 -17435.617428 -10454.351879 0.0000000
## Skoda-Jaguar -4086.926537 -7387.006575 -786.846500 0.0007656
## SsangYong-Jaguar -10093.677595 -14256.365942 -5930.989248 0.0000000
## Subaru-Jaguar -10290.905395 -13792.236679 -6789.574112 0.0000000
## Suzuki-Jaguar -12510.401239 -16076.829537 -8943.972942 0.0000000
## Toyota-Jaguar -8014.847544 -11302.955226 -4726.739863 0.0000000
## UAZ-Jaguar -14369.876216 -18588.652325 -10151.100107 0.0000000
## Volkswagen-Jaguar -11385.239179 -14625.629761 -8144.848598 0.0000000
## Volvo-Jaguar -9396.658460 -12733.252159 -6060.064762 0.0000000
## ZAZ-Jaguar -16452.788810 -21296.047147 -11609.530472 0.0000000
## Kia-Jeep -2756.393913 -5152.114672 -360.673154 0.0041632
## LADA-Jeep -3313.808069 -6297.337704 -330.278434 0.0084859
## Lancia-Jeep -8010.981070 -11344.317493 -4677.644647 0.0000000
## Land Rover-Jeep 4282.810941 1432.553381 7133.068501 0.0000019
## Lexus-Jeep 6218.071406 3440.074800 8996.068011 0.0000000
## Lifan-Jeep -2631.953269 -6734.541735 1470.635197 0.9355891
## Lincoln-Jeep -1175.017217 -5692.154520 3342.120086 1.0000000
## Mazda-Jeep -6180.962338 -8536.952303 -3824.972373 0.0000000
## Mercedes-Benz-Jeep -1522.672367 -3842.695605 797.350871 0.9122320
## Mini-Jeep 2221.211443 -1414.681534 5857.104421 0.9693222
## Mitsubishi-Jeep -5493.027250 -7892.436267 -3093.618233 0.0000000
## Moskvitch-Jeep -9933.577257 -13823.336181 -6043.818334 0.0000000
## Nissan-Jeep -4500.975266 -6854.834505 -2147.116027 0.0000000
## Opel-Jeep -6486.496540 -8796.479446 -4176.513634 0.0000000
## Peugeot-Jeep -6530.271713 -8859.375400 -4201.168025 0.0000000
## Pontiac-Jeep -6548.945392 -10817.835865 -2280.054918 0.0000009
## Porsche-Jeep 7717.334331 3956.049291 11478.619371 0.0000000
## Renault-Jeep -6353.265768 -8667.864143 -4038.667392 0.0000000
## Rover-Jeep -9178.247226 -11912.418054 -6444.076399 0.0000000
## Saab-Jeep -6781.904069 -9979.723046 -3584.085092 0.0000000
## Seat-Jeep -7044.474093 -9680.909511 -4408.038674 0.0000000
## Skoda-Jeep 2813.584023 435.180608 5191.987438 0.0022719
## SsangYong-Jeep -3193.167034 -6670.845689 284.511621 0.1484416
## Subaru-Jeep -3390.394834 -6040.978790 -739.810879 0.0003314
## Suzuki-Jeep -5609.890679 -8345.888732 -2873.892625 0.0000000
## Toyota-Jeep -1114.336983 -3476.100450 1247.426484 0.9998942
## UAZ-Jeep -7469.365655 -11013.987837 -3924.743474 0.0000000
## Volkswagen-Jeep -4484.728619 -6779.593684 -2189.863553 0.0000000
## Volvo-Jeep -2496.147900 -4924.960791 -67.335008 0.0330525
## ZAZ-Jeep -9552.278249 -13821.168722 -5283.387775 0.0000000
## LADA-Kia -557.414156 -2647.227024 1532.398711 1.0000000
## Lancia-Kia -5254.587157 -7819.153484 -2690.020829 0.0000000
## Land Rover-Kia 7039.204854 5144.518364 8933.891345 0.0000000
## Lexus-Kia 8974.465319 7190.331389 10758.599248 0.0000000
## Lifan-Kia 124.440644 -3382.281595 3631.162883 1.0000000
## Lincoln-Kia 1581.376696 -2402.390134 5565.143526 0.9999995
## Mazda-Kia -3424.568425 -4432.813165 -2416.323686 0.0000000
## Mercedes-Benz-Kia 1233.721546 312.647953 2154.795138 0.0000903
## Mini-Kia 4977.605356 2030.473448 7924.737264 0.0000000
## Mitsubishi-Kia -2736.633337 -3842.541276 -1630.725398 0.0000000
## Moskvitch-Kia -7177.183344 -10432.347430 -3922.019259 0.0000000
## Nissan-Kia -1744.581353 -2747.837081 -741.325624 0.0000000
## Opel-Kia -3730.102627 -4625.585605 -2834.619649 0.0000000
## Peugeot-Kia -3773.877800 -4717.590005 -2830.165595 0.0000000
## Pontiac-Kia -3792.551479 -7492.456155 -92.646802 0.0344354
## Porsche-Kia 10473.728244 7373.222819 13574.233669 0.0000000
## Renault-Kia -3596.871855 -4504.194499 -2689.549211 0.0000000
## Rover-Kia -6421.853314 -8136.950643 -4706.755984 0.0000000
## Saab-Kia -4025.510156 -6411.283605 -1639.736707 0.0000000
## Seat-Kia -4288.080180 -5842.641357 -2733.519003 0.0000000
## Skoda-Kia 5569.977936 4510.415978 6629.539895 0.0000000
## SsangYong-Kia -436.773121 -3186.339917 2312.793675 1.0000000
## Subaru-Kia -634.000922 -2212.438138 944.436295 0.9999992
## Suzuki-Kia -2853.496766 -4571.505522 -1135.488009 0.0000000
## Toyota-Kia 1642.056930 620.393867 2663.719992 0.0000001
## UAZ-Kia -4712.971743 -7546.735167 -1879.208318 0.0000000
## Volkswagen-Kia -1728.334706 -2584.065041 -872.604370 0.0000000
## Volvo-Kia 260.246013 -908.086474 1428.578500 1.0000000
## ZAZ-Kia -6795.884336 -10495.789012 -3095.979660 0.0000000
## Lancia-LADA -4697.173000 -7817.905538 -1576.440462 0.0000018
## Land Rover-LADA 7596.619011 4998.194868 10195.043153 0.0000000
## Lexus-LADA 9531.879475 7012.930289 12050.828662 0.0000000
## Lifan-LADA 681.854800 -3249.947838 4613.657439 1.0000000
## Lincoln-LADA 2138.790852 -2223.819523 6501.401227 0.9997062
## Mazda-LADA -2867.154269 -4911.299220 -823.009318 0.0000215
## Mercedes-Benz-LADA 1791.135702 -211.449549 3793.720954 0.1954875
## Mini-LADA 5535.019513 2092.991388 8977.047637 0.0000001
## Mitsubishi-LADA -2179.219180 -4273.259174 -85.179187 0.0270442
## Moskvitch-LADA -6619.769188 -10328.955669 -2910.582707 0.0000000
## Nissan-LADA -1187.167196 -3228.856003 854.521610 0.9869881
## Opel-LADA -3172.688470 -5163.633193 -1181.743747 0.0000002
## Peugeot-LADA -3216.463643 -5229.561716 -1203.365570 0.0000002
## Pontiac-LADA -3235.137322 -7340.166645 869.892000 0.5162748
## Porsche-LADA 11031.142401 7456.914510 14605.370292 0.0000000
## Renault-LADA -3039.457698 -5035.755648 -1043.159748 0.0000012
## Rover-LADA -5864.439157 -8334.971484 -3393.906831 0.0000000
## Saab-LADA -3468.095999 -6443.644029 -492.547970 0.0031559
## Seat-LADA -3730.666023 -6092.578719 -1368.753328 0.0000003
## Skoda-LADA 6127.392093 4057.454251 8197.329935 0.0000000
## SsangYong-LADA 120.641035 -3153.819026 3395.101096 1.0000000
## Subaru-LADA -76.586765 -2454.282114 2301.108584 1.0000000
## Suzuki-LADA -2296.082609 -4768.637000 176.471781 0.1307398
## Toyota-LADA 2199.471086 148.674524 4250.267648 0.0162158
## UAZ-LADA -4155.557586 -7501.030090 -810.085082 0.0007103
## Volkswagen-LADA -1170.920549 -3144.304833 802.463735 0.9812616
## Volvo-LADA 817.660170 -1310.008020 2945.328359 0.9999998
## ZAZ-LADA -6238.470179 -10343.499502 -2133.440857 0.0000013
## Land Rover-Lancia 12293.792011 9300.217116 15287.366905 0.0000000
## Lexus-Lancia 14229.052476 11304.195619 17153.909332 0.0000000
## Lifan-Lancia 5379.027801 1175.606026 9582.449575 0.0003275
## Lincoln-Lancia 6835.963853 2227.053796 11444.873909 0.0000031
## Mazda-Lancia 1830.018731 -697.472358 4357.509820 0.7414507
## Mercedes-Benz-Lancia 6488.308702 3994.309842 8982.307563 0.0000000
## Mini-Lancia 10232.192513 6482.893881 13981.491145 0.0000000
## Mitsubishi-Lancia 2517.953820 -50.058275 5085.965915 0.0660196
## Moskvitch-Lancia -1922.596188 -5918.562526 2073.370150 0.9998206
## Nissan-Lancia 3510.005804 984.500744 6035.510864 0.0000289
## Opel-Lancia 1524.484530 -960.177120 4009.146180 0.9671034
## Peugeot-Lancia 1480.709357 -1021.738740 3983.157454 0.9821601
## Pontiac-Lancia 1462.035678 -2903.848925 5827.920281 1.0000000
## Porsche-Lancia 15728.315401 11857.296460 19599.334342 0.0000000
## Renault-Lancia 1657.715302 -831.237918 4146.668522 0.8941317
## Rover-Lancia -1167.266157 -4050.530384 1715.998070 0.9999990
## Saab-Lancia 1229.077001 -2097.117326 4555.271328 1.0000000
## Seat-Lancia 966.506977 -1824.248155 3757.262109 1.0000000
## Skoda-Lancia 10824.565093 8276.168480 13372.961706 0.0000000
## SsangYong-Lancia 4817.814036 1221.736823 8413.891248 0.0000897
## Subaru-Lancia 4620.586235 1816.461096 7424.711375 0.0000000
## Suzuki-Lancia 2401.090391 -483.906635 5286.087417 0.3640619
## Toyota-Lancia 6896.644086 4363.770389 9429.517783 0.0000000
## UAZ-Lancia 541.615414 -3119.240893 4202.471721 1.0000000
## Volkswagen-Lancia 3526.252451 1055.639539 5996.865363 0.0000121
## Volvo-Lancia 5514.833170 2919.326562 8110.339778 0.0000000
## ZAZ-Lancia -1541.297179 -5907.181782 2824.587424 1.0000000
## Lexus-Land Rover 1935.260465 -424.320871 4294.841801 0.4037990
## Lifan-Land Rover -6914.764210 -10746.420649 -3083.107772 0.0000000
## Lincoln-Land Rover -5457.828158 -9730.402132 -1185.254185 0.0003435
## Mazda-Land Rover -10463.773280 -12307.966235 -8619.580324 0.0000000
## Mercedes-Benz-Land Rover -5805.483308 -7603.500763 -4007.465854 0.0000000
## Mini-Land Rover -2061.599498 -5388.771974 1265.572978 0.9615544
## Mitsubishi-Land Rover -9775.838191 -11675.186124 -7876.490258 0.0000000
## Moskvitch-Land Rover -14216.388199 -17819.245873 -10613.530524 0.0000000
## Nissan-Land Rover -8783.786207 -10625.256342 -6942.316072 0.0000000
## Opel-Land Rover -10769.307481 -12554.350888 -8984.264073 0.0000000
## Peugeot-Land Rover -10813.082654 -12622.801673 -9003.363635 0.0000000
## Pontiac-Land Rover -10831.756333 -14840.968826 -6822.543840 0.0000000
## Porsche-Land Rover 3434.523390 -29.235275 6898.282056 0.0564708
## Renault-Land Rover -10636.076709 -12427.088876 -8845.064542 0.0000000
## Rover-Land Rover -13461.058168 -15768.881612 -11153.234723 0.0000000
## Saab-Land Rover -11064.715010 -13906.616689 -8222.813331 0.0000000
## Seat-Land Rover -11327.285034 -13518.437848 -9136.132220 0.0000000
## Skoda-Land Rover -1469.226918 -3341.968690 403.514854 0.5291439
## SsangYong-Land Rover -7475.977975 -10629.485192 -4322.470759 0.0000000
## Subaru-Land Rover -7673.205776 -9881.362076 -5465.049475 0.0000000
## Suzuki-Land Rover -9892.701620 -12202.689561 -7582.713678 0.0000000
## Toyota-Land Rover -5397.147925 -7248.710944 -3545.584905 0.0000000
## UAZ-Land Rover -11752.176597 -14979.358858 -8524.994335 0.0000000
## Volkswagen-Land Rover -8767.539560 -10532.975656 -7002.103464 0.0000000
## Volvo-Land Rover -6778.958841 -8715.319087 -4842.598595 0.0000000
## ZAZ-Land Rover -13835.089190 -17844.301683 -9825.876697 0.0000000
## Lifan-Lexus -8850.024675 -12628.236956 -5071.812393 0.0000000
## Lincoln-Lexus -7393.088623 -11617.799896 -3168.377350 0.0000000
## Mazda-Lexus -12399.033744 -14129.451219 -10668.616269 0.0000000
## Mercedes-Benz-Lexus -7740.743773 -9421.863563 -6059.623984 0.0000000
## Mini-Lexus -3996.859963 -7262.342112 -731.377814 0.0010229
## Mitsubishi-Lexus -11711.098655 -13500.182095 -9922.015216 0.0000000
## Moskvitch-Lexus -16151.648663 -19697.615443 -12605.681883 0.0000000
## Nissan-Lexus -10719.046672 -12446.562009 -8991.531334 0.0000000
## Opel-Lexus -12704.567945 -14371.804266 -11037.331625 0.0000000
## Peugeot-Lexus -12748.343119 -14441.972331 -11054.713906 0.0000000
## Pontiac-Lexus -12767.016797 -16725.183266 -8808.850328 0.0000000
## Porsche-Lexus 1499.262925 -1905.281255 4903.807106 0.9999855
## Renault-Lexus -12571.337173 -14244.962449 -10897.711898 0.0000000
## Rover-Lexus -15396.318632 -17614.278513 -13178.358751 0.0000000
## Saab-Lexus -12999.975475 -15769.398182 -10230.552767 0.0000000
## Seat-Lexus -13262.545499 -15358.839285 -11166.251713 0.0000000
## Skoda-Lexus -3404.487382 -5165.299334 -1643.675431 0.0000000
## SsangYong-Lexus -9411.238440 -12499.588282 -6322.888598 0.0000000
## Subaru-Lexus -9608.466240 -11722.526606 -7494.405875 0.0000000
## Suzuki-Lexus -11827.962084 -14048.174072 -9607.750097 0.0000000
## Toyota-Lexus -7332.408389 -9070.678390 -5594.138388 0.0000000
## UAZ-Lexus -13687.437061 -16850.979748 -10523.894375 0.0000000
## Volkswagen-Lexus -10702.800025 -12349.026495 -9056.573554 0.0000000
## Volvo-Lexus -8714.219306 -10542.548593 -6885.890018 0.0000000
## ZAZ-Lexus -15770.349655 -19728.516124 -11812.183186 0.0000000
## Lincoln-Lifan 1456.936052 -3735.573199 6649.445303 1.0000000
## Mazda-Lifan -3549.009069 -7028.709002 -69.309137 0.0372180
## Mercedes-Benz-Lifan 1109.280902 -2346.168523 4564.730327 1.0000000
## Mini-Lifan 4853.164712 405.992504 9300.336920 0.0118440
## Mitsubishi-Lifan -2861.073981 -6370.316995 648.169034 0.4203071
## Moskvitch-Lifan -7301.623988 -11958.645288 -2644.602688 0.0000004
## Nissan-Lifan -1869.021997 -5347.279639 1609.235645 0.9973614
## Opel-Lifan -3854.543271 -7303.259543 -405.826998 0.0075114
## Peugeot-Lifan -3898.318444 -7359.871114 -436.765773 0.0064635
## Pontiac-Lifan -3916.992123 -8895.048850 1061.064605 0.5207528
## Porsche-Lifan 10349.287600 5799.025184 14899.550017 0.0000000
## Renault-Lifan -3721.312498 -7173.121958 -269.503039 0.0147853
## Rover-Lifan -6546.293957 -10292.400338 -2800.187577 0.0000000
## Saab-Lifan -4149.950800 -8246.738458 -53.163142 0.0413032
## Seat-Lifan -4412.520824 -8087.900344 -737.141303 0.0016203
## Skoda-Lifan 5445.537292 1950.623029 8940.451556 0.0000005
## SsangYong-Lifan -561.213765 -4879.995044 3757.567513 1.0000000
## Subaru-Lifan -758.441565 -4443.983345 2927.100215 1.0000000
## Suzuki-Lifan -2977.937410 -6725.377636 769.502817 0.4926531
## Toyota-Lifan 1517.616286 -1965.995286 5001.227857 0.9999895
## UAZ-Lifan -4837.412386 -9210.279788 -464.544985 0.0091701
## Volkswagen-Lifan -1852.775350 -5291.383872 1585.833173 0.9971986
## Volvo-Lifan 135.805369 -3393.607467 3665.218205 1.0000000
## ZAZ-Lifan -6920.324980 -11898.381707 -1942.268253 0.0000287
## Mazda-Lincoln -5005.945121 -8965.946247 -1045.943996 0.0004529
## Mercedes-Benz-Lincoln -347.655150 -4286.364079 3591.053778 1.0000000
## Mini-Lincoln 3396.228660 -1436.021121 8228.478441 0.8055568
## Mitsubishi-Lincoln -4318.010033 -8303.995962 -332.024103 0.0135516
## Moskvitch-Lincoln -8758.560040 -13784.606666 -3732.513415 0.0000000
## Nissan-Lincoln -3325.958049 -7284.691877 632.775779 0.3392060
## Opel-Lincoln -5311.479323 -9244.282550 -1378.676095 0.0000705
## Peugeot-Lincoln -5355.254496 -9299.318921 -1411.190071 0.0000600
## Pontiac-Lincoln -5373.928175 -10698.808086 -49.048263 0.0436795
## Porsche-Lincoln 8892.351548 3965.061689 13819.641407 0.0000000
## Renault-Lincoln -5178.248550 -9113.764507 -1242.732594 0.0001516
## Rover-Lincoln -8003.230009 -12199.253164 -3807.206855 0.0000000
## Saab-Lincoln -5606.886852 -10118.756355 -1095.017349 0.0007024
## Seat-Lincoln -5869.456876 -10002.459648 -1736.454103 0.0000143
## Skoda-Lincoln 3988.601240 15.224464 7961.978017 0.0472805
## SsangYong-Lincoln -2018.149817 -6732.507571 2696.207936 0.9999939
## Subaru-Lincoln -2215.377617 -6357.420049 1926.664814 0.9976187
## Suzuki-Lincoln -4434.873462 -8632.087484 -237.659439 0.0209974
## Toyota-Lincoln 60.680234 -3902.758535 4024.119003 1.0000000
## UAZ-Lincoln -6294.348438 -11058.303372 -1530.393505 0.0001346
## Volkswagen-Lincoln -3309.711402 -7233.654045 614.231242 0.3290519
## Volvo-Lincoln -1321.130683 -5324.885453 2682.624088 1.0000000
## ZAZ-Lincoln -8377.261032 -13702.140944 -3052.381120 0.0000003
## Mercedes-Benz-Mazda 4658.289971 3846.140156 5470.439786 0.0000000
## Mini-Mazda 8402.173781 5487.247154 11317.100408 0.0000000
## Mitsubishi-Mazda 687.935089 -329.042358 1704.912536 0.8722236
## Moskvitch-Mazda -3752.614919 -6978.650254 -526.579584 0.0032942
## Nissan-Mazda 1679.987072 775.701268 2584.272877 0.0000000
## Opel-Mazda -305.534201 -1088.541559 477.473157 0.9999997
## Peugeot-Mazda -349.309374 -1187.046496 488.427747 0.9999973
## Pontiac-Mazda -367.983053 -4042.286439 3306.320333 1.0000000
## Porsche-Mazda 13898.296670 10828.387119 16968.206220 0.0000000
## Renault-Mazda -172.303429 -968.824075 624.217216 1.0000000
## Rover-Mazda -2997.284888 -4656.432142 -1338.137635 0.0000000
## Saab-Mazda -600.941731 -2946.815919 1744.932458 1.0000000
## Seat-Mazda -863.511754 -2356.117266 629.093757 0.9881564
## Skoda-Mazda 8994.546362 8028.170502 9960.922221 0.0000000
## SsangYong-Mazda 2987.795304 272.776276 5702.814332 0.0101279
## Subaru-Mazda 2790.567504 1273.110814 4308.024193 0.0000000
## Suzuki-Mazda 571.071660 -1091.085025 2233.228345 1.0000000
## Toyota-Mazda 5066.625355 4141.959917 5991.290793 0.0000000
## UAZ-Mazda -1288.403317 -4088.657932 1511.851297 0.9999476
## Volkswagen-Mazda 1696.233720 959.019256 2433.448184 0.0000000
## Volvo-Mazda 3684.814439 2600.281563 4769.347314 0.0000000
## ZAZ-Mazda -3371.315910 -7045.619296 302.987475 0.1496025
## Mini-Mercedes-Benz 3743.883810 857.949586 6629.818035 0.0002256
## Mitsubishi-Mercedes-Benz -3970.354882 -4900.979531 -3039.730233 0.0000000
## Moskvitch-Mercedes-Benz -8410.904890 -11610.767870 -5211.041910 0.0000000
## Nissan-Mercedes-Benz -2978.302899 -3784.250741 -2172.355056 0.0000000
## Opel-Mercedes-Benz -4963.824172 -5630.847135 -4296.801210 0.0000000
## Peugeot-Mercedes-Benz -5007.599345 -5738.094105 -4277.104586 0.0000000
## Pontiac-Mercedes-Benz -5026.273024 -8677.618596 -1374.927453 0.0000392
## Porsche-Mercedes-Benz 9240.006699 6197.612286 12282.401111 0.0000000
## Renault-Mercedes-Benz -4830.593400 -5513.428848 -4147.757953 0.0000000
## Rover-Mercedes-Benz -7655.574859 -9263.240471 -6047.909248 0.0000000
## Saab-Mercedes-Benz -5259.231702 -7568.981649 -2949.481754 0.0000000
## Seat-Mercedes-Benz -5521.801725 -6956.963849 -4086.639602 0.0000000
## Skoda-Mercedes-Benz 4336.256391 3461.212728 5211.300053 0.0000000
## SsangYong-Mercedes-Benz -1670.494667 -4354.362665 1013.373331 0.9587720
## Subaru-Mercedes-Benz -1867.722467 -3328.713197 -406.731737 0.0003364
## Suzuki-Mercedes-Benz -4087.218311 -5697.989541 -2476.447081 0.0000000
## Toyota-Mercedes-Benz 408.335384 -420.413843 1237.084611 0.9996658
## UAZ-Mercedes-Benz -5946.693288 -8716.755565 -3176.631012 0.0000000
## Volkswagen-Mercedes-Benz -2962.056251 -3574.677076 -2349.435427 0.0000000
## Volvo-Mercedes-Benz -973.475532 -1977.482837 30.531772 0.0767602
## ZAZ-Mercedes-Benz -8029.605881 -11680.951453 -4378.260310 0.0000000
## Mitsubishi-Mini -7714.238693 -10664.369564 -4764.107822 0.0000000
## Moskvitch-Mini -12154.788701 -16406.415802 -7903.161599 0.0000000
## Nissan-Mini -6722.186709 -9635.391447 -3808.981971 0.0000000
## Opel-Mini -8707.707983 -11585.576908 -5829.839058 0.0000000
## Peugeot-Mini -8751.483156 -11644.722260 -5858.244052 0.0000000
## Pontiac-Mini -8770.156835 -13371.193066 -4169.120604 0.0000000
## Porsche-Mini 5496.122888 1361.709547 9630.536230 0.0001127
## Renault-Mini -8574.477211 -11456.052156 -5692.902265 0.0000000
## Rover-Mini -11399.458670 -14627.739735 -8171.177604 0.0000000
## Saab-Mini -9003.115512 -12632.461836 -5373.769188 0.0000000
## Seat-Mini -9265.685536 -12411.619458 -6119.751614 0.0000000
## Skoda-Mini 592.372580 -2340.699415 3525.444576 1.0000000
## SsangYong-Mini -5414.378477 -9292.568435 -1536.188519 0.0000250
## Subaru-Mini -5611.606278 -8769.406751 -2453.805804 0.0000000
## Suzuki-Mini -7831.102122 -11060.930890 -4601.273353 0.0000000
## Toyota-Mini -3335.548427 -6255.143467 -415.953386 0.0048240
## UAZ-Mini -9690.577099 -13628.908590 -5752.245607 0.0000000
## Volkswagen-Mini -6705.940062 -9571.688514 -3840.191609 0.0000000
## Volvo-Mini -4717.359343 -7691.454262 -1743.264424 0.0000002
## ZAZ-Mini -11773.489692 -16374.525923 -7172.453461 0.0000000
## Moskvitch-Mitsubishi -4440.550008 -7698.429517 -1182.670499 0.0000533
## Nissan-Mitsubishi 992.051984 -19.979503 2004.083471 0.0662495
## Opel-Mitsubishi -993.469290 -1898.773348 -88.165232 0.0106769
## Peugeot-Mitsubishi -1037.244463 -1990.280874 -84.208052 0.0124465
## Pontiac-Mitsubishi -1055.918142 -4758.212064 2646.375780 1.0000000
## Porsche-Mitsubishi 13210.361581 10107.005403 16313.717758 0.0000000
## Renault-Mitsubishi -860.238518 -1777.255450 56.778414 0.1161701
## Rover-Mitsubishi -3685.219977 -5405.465461 -1964.974493 0.0000000
## Saab-Mitsubishi -1288.876819 -3678.353880 1100.600242 0.9971318
## Seat-Mitsubishi -1551.446843 -3111.685968 8.792282 0.0542447
## Skoda-Mitsubishi 8306.611273 7238.736178 9374.486368 0.0000000
## SsangYong-Mitsubishi 2299.860215 -452.920785 5052.641216 0.3538729
## Subaru-Mitsubishi 2102.632415 518.602832 3686.661998 0.0001177
## Suzuki-Mitsubishi -116.863429 -1840.011641 1606.284783 1.0000000
## Toyota-Mitsubishi 4378.690266 3348.408224 5408.972309 0.0000000
## UAZ-Mitsubishi -1976.338406 -4813.220642 860.543830 0.8225290
## Volkswagen-Mitsubishi 1008.298631 142.296275 1874.300987 0.0032283
## Volvo-Mitsubishi 2996.879350 1821.002454 4172.756246 0.0000000
## ZAZ-Mitsubishi -4059.250999 -7761.544921 -356.957077 0.0108557
## Nissan-Moskvitch 5432.601992 2208.122408 8657.081575 0.0000000
## Opel-Moskvitch 3447.080718 254.489875 6639.671561 0.0143880
## Peugeot-Moskvitch 3403.305545 196.852792 6609.758297 0.0194418
## Pontiac-Moskvitch 3384.631866 -1419.537276 8188.801007 0.8007550
## Porsche-Moskvitch 17650.911589 13291.567553 22010.255624 0.0000000
## Renault-Moskvitch 3580.311490 384.379559 6776.243421 0.0071757
## Rover-Moskvitch 755.330031 -2756.408196 4267.068258 1.0000000
## Saab-Moskvitch 3151.673189 -731.967047 7035.313424 0.4332515
## Seat-Moskvitch 2889.103165 -547.087601 6325.293931 0.3372613
## Skoda-Moskvitch 12747.161281 9504.721139 15989.601422 0.0000000
## SsangYong-Moskvitch 6740.410223 2623.267514 10857.552933 0.0000001
## Subaru-Moskvitch 6543.182423 3096.124174 9990.240672 0.0000000
## Suzuki-Moskvitch 4323.686579 810.525521 7836.847636 0.0008942
## Toyota-Moskvitch 8819.240274 5588.986114 12049.494434 0.0000000
## UAZ-Moskvitch 2464.211602 -1709.630953 6638.054157 0.9828478
## Volkswagen-Moskvitch 5448.848639 2267.179119 8630.518159 0.0000000
## Volvo-Moskvitch 7437.429358 4157.833753 10717.024962 0.0000000
## ZAZ-Moskvitch 381.299009 -4422.870133 5185.468150 1.0000000
## Opel-Nissan -1985.521274 -2762.093953 -1208.948595 0.0000000
## Peugeot-Nissan -2029.296447 -2861.022416 -1197.570478 0.0000000
## Pontiac-Nissan -2047.970126 -5720.907639 1624.967387 0.9942324
## Porsche-Nissan 12218.309597 9150.034959 15286.584235 0.0000000
## Renault-Nissan -1852.290502 -2642.486516 -1062.094487 0.0000000
## Rover-Nissan -4677.271961 -6333.392189 -3021.151732 0.0000000
## Saab-Nissan -2280.928803 -4624.663070 62.805464 0.0730542
## Seat-Nissan -2543.498827 -4032.738840 -1054.258814 0.0000000
## Skoda-Nissan 7314.559289 6353.389739 8275.728840 0.0000000
## SsangYong-Nissan 1307.808232 -1405.362041 4020.978504 0.9998115
## Subaru-Nissan 1110.580431 -403.565997 2624.726860 0.7105451
## Suzuki-Nissan -1108.915413 -2768.050564 550.219738 0.8894359
## Toyota-Nissan 3386.638282 2467.415364 4305.861201 0.0000000
## UAZ-Nissan -2968.390390 -5766.852558 -169.928221 0.0196541
## Volkswagen-Nissan 16.246647 -714.129810 746.623104 1.0000000
## Volvo-Nissan 2004.827366 924.931000 3084.723732 0.0000000
## ZAZ-Nissan -5051.302983 -8724.240496 -1378.365470 0.0000404
## Peugeot-Opel -43.775173 -741.726314 654.175968 1.0000000
## Pontiac-Opel -62.448852 -3707.423157 3582.525453 1.0000000
## Porsche-Opel 14203.830871 11169.085913 17238.575829 0.0000000
## Renault-Opel 133.230772 -514.671573 781.133117 1.0000000
## Rover-Opel -2691.750687 -4284.892840 -1098.608534 0.0000000
## Saab-Opel -295.407529 -2595.072292 2004.257233 1.0000000
## Seat-Opel -557.977553 -1976.851586 860.896480 0.9999996
## Skoda-Opel 9300.080563 8452.015346 10148.145780 0.0000000
## SsangYong-Opel 3293.329505 618.135936 5968.523075 0.0008879
## Subaru-Opel 3096.101705 1651.107894 4541.095516 0.0000000
## Suzuki-Opel 876.605861 -719.670167 2472.881889 0.9957900
## Toyota-Opel 5372.159556 4571.948033 6172.371079 0.0000000
## UAZ-Opel -982.869116 -3744.527715 1778.789483 1.0000000
## Volkswagen-Opel 2001.767921 1428.341835 2575.194007 0.0000000
## Volvo-Opel 3990.348640 3009.765207 4970.932073 0.0000000
## ZAZ-Opel -3065.781709 -6710.756014 579.192596 0.3363006
## Pontiac-Peugeot -18.673679 -3675.795586 3638.448228 1.0000000
## Porsche-Peugeot 14247.606044 11198.281541 17296.930546 0.0000000
## Renault-Peugeot 177.005945 -536.072179 890.084069 1.0000000
## Rover-Peugeot -2647.975514 -4268.717591 -1027.233437 0.0000001
## Saab-Peugeot -251.632356 -2570.502982 2067.238270 1.0000000
## Seat-Peugeot -514.202380 -1963.997705 935.592945 1.0000000
## Skoda-Peugeot 9343.855736 8445.013384 10242.698088 0.0000000
## SsangYong-Peugeot 3337.104679 645.383371 6028.825986 0.0007453
## Subaru-Peugeot 3139.876878 1664.509102 4615.244655 0.0000000
## Suzuki-Peugeot 920.381034 -703.441653 2544.203721 0.9919986
## Toyota-Peugeot 5415.934729 4562.095492 6269.773967 0.0000000
## UAZ-Peugeot -939.093943 -3716.765842 1838.577956 1.0000000
## Volkswagen-Peugeot 2045.543094 1399.384880 2691.701308 0.0000000
## Volvo-Peugeot 4034.123813 3009.308302 5058.939324 0.0000000
## ZAZ-Peugeot -3022.006536 -6679.128443 635.115371 0.3833205
## Porsche-Pontiac 14266.279723 9565.526399 18967.033046 0.0000000
## Renault-Pontiac 195.679624 -3452.221457 3843.580705 1.0000000
## Rover-Pontiac -2629.301835 -6556.833728 1298.230058 0.8872161
## Saab-Pontiac -232.958677 -4496.274625 4030.357270 1.0000000
## Seat-Pontiac -495.528701 -4355.659317 3364.601915 1.0000000
## Skoda-Pontiac 9362.529415 5673.814265 13051.244565 0.0000000
## SsangYong-Pontiac 3355.778357 -1121.281558 7832.838272 0.6552194
## Subaru-Pontiac 3158.550557 -711.257185 7028.358300 0.4172162
## Suzuki-Pontiac 939.054713 -2989.749431 4867.858857 1.0000000
## Toyota-Pontiac 5434.608408 1756.600343 9112.616474 0.0000036
## UAZ-Pontiac -920.420264 -5449.676612 3608.836084 1.0000000
## Volkswagen-Pontiac 2064.216773 -1571.195490 5699.629036 0.9917152
## Volvo-Pontiac 4052.797492 331.379924 7774.215060 0.0123026
## ZAZ-Pontiac -3003.332857 -8119.311994 2112.646280 0.9845072
## Renault-Porsche -14070.600099 -17108.859728 -11032.340469 0.0000000
## Rover-Porsche -16895.581558 -20264.460607 -13526.702509 0.0000000
## Saab-Porsche -14499.238400 -18254.195410 -10744.281390 0.0000000
## Seat-Porsche -14761.808424 -18051.861258 -11471.755590 0.0000000
## Skoda-Porsche -4903.750308 -7990.894415 -1816.606201 0.0000002
## SsangYong-Porsche -10910.501365 -14906.487447 -6914.515284 0.0000000
## Subaru-Porsche -11107.729166 -14409.130571 -7806.327760 0.0000000
## Suzuki-Porsche -13327.225010 -16697.587199 -9956.862821 0.0000000
## Toyota-Porsche -8831.671315 -11906.013945 -5757.328684 0.0000000
## UAZ-Porsche -15186.699987 -19241.080623 -11132.319351 0.0000000
## Volkswagen-Porsche -12202.062950 -15225.316430 -9178.809470 0.0000000
## Volvo-Porsche -10213.482231 -13339.628100 -7087.336362 0.0000000
## ZAZ-Porsche -17269.612580 -21970.365904 -12568.859256 0.0000000
## Rover-Renault -2824.981459 -4424.808492 -1225.154426 0.0000000
## Saab-Renault -428.638301 -2732.939201 1875.662598 1.0000000
## Seat-Renault -691.208325 -2117.584201 735.167550 0.9997831
## Skoda-Renault 9166.849791 8306.292281 10027.407301 0.0000000
## SsangYong-Renault 3160.098733 480.918775 5839.278692 0.0024267
## Subaru-Renault 2962.870933 1510.510189 4415.231677 0.0000000
## Suzuki-Renault 743.375089 -859.572749 2346.322927 0.9999345
## Toyota-Renault 5238.928784 4425.489728 6052.367840 0.0000000
## UAZ-Renault -1116.099888 -3881.620243 1649.420467 0.9999991
## Volkswagen-Renault 1868.537149 1276.792194 2460.282104 0.0000000
## Volvo-Renault 3857.117868 2865.710542 4848.525193 0.0000000
## ZAZ-Renault -3199.012481 -6846.913562 448.888600 0.2362584
## Saab-Rover 2396.343158 -329.115906 5121.802221 0.2305897
## Seat-Rover 2133.773134 95.913323 4171.632945 0.0244561
## Skoda-Rover 11991.831250 10301.007871 13682.654629 0.0000000
## SsangYong-Rover 5985.080192 2936.091881 9034.068503 0.0000000
## Subaru-Rover 5787.852392 3731.721024 7843.983760 0.0000000
## Suzuki-Rover 3568.356548 1403.231553 5733.481543 0.0000000
## Toyota-Rover 8063.910243 6396.574774 9731.245713 0.0000000
## UAZ-Rover 1708.881571 -1416.246795 4834.009937 0.9961523
## Volkswagen-Rover 4693.518608 3122.376806 6264.660410 0.0000000
## Volvo-Rover 6682.099327 4921.073218 8443.125435 0.0000000
## ZAZ-Rover -374.031022 -4301.562915 3553.500870 1.0000000
## Seat-Saab -262.570024 -2889.969634 2364.829586 1.0000000
## Skoda-Saab 9595.488092 7227.104720 11963.871465 0.0000000
## SsangYong-Saab 3588.737035 117.903443 7059.570626 0.0300215
## Subaru-Saab 3391.509234 749.912690 6033.105778 0.0002998
## Suzuki-Saab 1172.013390 -1555.278736 3899.305517 0.9999931
## Toyota-Saab 5667.567085 3315.894560 8019.239611 0.0000000
## UAZ-Saab -687.461587 -4225.368228 2850.445055 1.0000000
## Volkswagen-Saab 2297.175450 12.696809 4581.654092 0.0460979
## Volvo-Saab 4285.756169 1866.754504 6704.757834 0.0000000
## ZAZ-Saab -2770.374180 -7033.690127 1492.941768 0.9231164
## Skoda-Seat 9858.058116 8330.319492 11385.796740 0.0000000
## SsangYong-Seat 3851.307059 889.646828 6812.967289 0.0002112
## Subaru-Seat 3654.079258 1729.818932 5578.339585 0.0000000
## Suzuki-Seat 1434.583414 -605.727309 3474.894137 0.8047263
## Toyota-Seat 5930.137109 4428.435018 7431.839201 0.0000000
## UAZ-Seat -424.891563 -3464.879870 2615.096744 1.0000000
## Volkswagen-Seat 2559.745474 1165.619165 3953.871783 0.0000000
## Volvo-Seat 4548.326193 2943.235990 6153.416396 0.0000000
## ZAZ-Seat -2507.804156 -6367.934772 1352.326460 0.9233538
## SsangYong-Skoda -6006.751058 -8741.242311 -3272.259805 0.0000000
## Subaru-Skoda -6203.978858 -7756.006207 -4651.951508 0.0000000
## Suzuki-Skoda -8423.474702 -10117.251233 -6729.698171 0.0000000
## Toyota-Skoda -3927.921007 -4908.288418 -2947.553595 0.0000000
## UAZ-Skoda -10282.949679 -13102.087844 -7463.811514 0.0000000
## Volkswagen-Skoda -7298.312642 -8104.289847 -6492.335437 0.0000000
## Volvo-Skoda -5309.731923 -6442.130108 -4177.333739 0.0000000
## ZAZ-Skoda -12365.862272 -16054.577423 -8677.147122 0.0000000
## Subaru-SsangYong -197.227800 -3171.489879 2777.034279 1.0000000
## Suzuki-SsangYong -2416.723644 -5467.350622 633.903333 0.5014539
## Toyota-SsangYong 2078.830051 -641.200516 4798.860617 0.6026456
## UAZ-SsangYong -4276.198621 -8068.953032 -483.444211 0.0063170
## Volkswagen-SsangYong -1291.561585 -3953.712083 1370.588914 0.9997763
## Volvo-SsangYong 697.019134 -2081.428574 3475.466843 1.0000000
## ZAZ-SsangYong -6359.111215 -10836.171130 -1882.051300 0.0000142
## Suzuki-Subaru -2219.495844 -4278.056370 -160.935318 0.0147601
## Toyota-Subaru 2276.057851 749.652674 3802.463028 0.0000026
## UAZ-Subaru -4078.970821 -7131.237601 -1026.704041 0.0000966
## Volkswagen-Subaru -1094.333784 -2515.034934 326.367366 0.5808875
## Volvo-Subaru 894.246935 -733.978526 2522.472395 0.9957805
## ZAZ-Subaru -6161.883414 -10031.691157 -2292.075672 0.0000002
## Toyota-Suzuki 4495.553695 2825.223547 6165.883844 0.0000000
## UAZ-Suzuki -1859.474977 -4986.202106 1267.252152 0.9805028
## Volkswagen-Suzuki 1125.162060 -449.157412 2699.481532 0.7707122
## Volvo-Suzuki 3113.742779 1349.881052 4877.604506 0.0000000
## ZAZ-Suzuki -3942.387570 -7871.191715 -13.583426 0.0475404
## UAZ-Toyota -6355.028672 -9160.142549 -3549.914795 0.0000000
## Volkswagen-Toyota -3370.391635 -4125.853828 -2614.929443 0.0000000
## Volvo-Toyota -1381.810916 -2478.829384 -284.792449 0.0004972
## ZAZ-Toyota -8437.941265 -12115.949331 -4759.933200 0.0000000
## Volkswagen-UAZ 2984.637037 235.611235 5733.662839 0.0130147
## Volvo-UAZ 4973.217756 2111.422995 7835.012516 0.0000000
## ZAZ-UAZ -2082.912593 -6612.168941 2446.343755 0.9999483
## Volvo-Volkswagen 1988.580719 1044.161050 2933.000388 0.0000000
## ZAZ-Volkswagen -5067.549630 -8702.961893 -1432.137367 0.0000263
## ZAZ-Volvo -7056.130349 -10777.547917 -3334.712781 0.0000000With the results see that numerous manufacturers are statistically different and we can use this data to list every statistically different manufacturer.
We can confirm that there is a relationship between the manufacturer and asking price.
Q3: What is the relationship between odometer and price?
A: There is a low negative correlation between price and odometer.
Justification:
Initially a Scatter plot was used to quickly inspect for possible relationships between price and odometer.
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
When we investigate this graph we initially notice that the line of best fit seems to be going down as the odometer value increases. Nevertheless, the data had a significant amount of variation and further testing would need to be done to confirm our results.
Since we are investigating the relationship between 2 continuous variables we begin by using a correlation test.
The hypotheses for the test are as follows:
H0 =There does not exist a correlation between Odometer Value and Vehicle Price
Ha = There does exist a correlation between Odometer Value and Vehicle Price
Furthermore, we will use 0.05 as our significance level. The result of the correlation test was the following:
#Getting cor value
cor.test(cars_edited$odometer_value, cars_edited$price_usd)##
## Pearson's product-moment correlation
##
## data: cars_edited$odometer_value and cars_edited$price_usd
## t = -90.821, df = 38488, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4282966 -0.4118421
## sample estimates:
## cor
## -0.4201039Since the p-value is less than 0.05 we can conclude that Price and Odometer are significantly correlated with a correlation coefficient of -0.4201039 and p-value of < 2.2e-16
We continue with our question by investigating how good of an indicator of price odometer is. To do this we will use linear regression, and check the percentage of accuracy of that line. Afterwords, we will graph the linear regression line to provide us with a useful visual.
## `geom_smooth()` using formula 'y ~ x'
Once the linear regression model is created and graphed we proceed to check the R2 to see the proportion of the prices that can be explained by the model, the variability of the beta coefficients, and the percentage error.
summary(odometer_on_price)##
## Call:
## lm(formula = price_usd ~ odometer_value, data = cars_edited)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11577 -3514 -1122 2064 40854
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.158e+04 6.203e+01 186.65 <2e-16 ***
## odometer_value -1.986e-02 2.186e-04 -90.82 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5830 on 38488 degrees of freedom
## Multiple R-squared: 0.1765, Adjusted R-squared: 0.1765
## F-statistic: 8248 on 1 and 38488 DF, p-value: < 2.2e-16confint(odometer_on_price)## 2.5 % 97.5 %
## (Intercept) 1.145654e+04 1.169971e+04
## odometer_value -2.028451e-02 -1.942748e-02# 2.5 % 97.5 %
#(Intercept) 1.145654e+04 1.169971e+04
#odometer_value -2.028451e-02 -1.942748e-02
sigma(odometer_on_price)*100/mean(cars_edited$price_usd)## [1] 87.89188#[1] 87.89188The R2 is 0.1765 which indicates that a low proportion of prices in the data can be explained by the model. Furthermore, the percentage error is 87.89188 which confirms how poor a model would be if it solely used odometer to predict price.
We can conclude that the higher the odometer the lower the price of the vehicle will be. Nevertheless, our linear regression model informs us that in order to create an accurate model we will need to consider more attributes.
Q4: Does the number of photos a vehicle has impact the selling price?
A: There exists a low positive correlation between number of photos a vehicle has and the selling price.
Justification:
In order to gain an intuitive understanding of the question we sought to use a scatter plot to see the relationship between price and number of photos.
#Scatter plot: Number of photos and price
ggplot(cars_edited, aes( x =number_of_photos, y=price_usd)) + geom_hex() + stat_smooth(color = "red") ## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
When we look at this graph we can see that although there exists a line. Nevertheless, the data had a significant amount of variation and further testing would need to be done to get a result. It does seem that there will be a very little(if any) correlation between price and number of photos.
Since we are investigating the relationship between 2 continuous variables we will be using the correlation test.
The hypotheses are as follows:
H0 =There does not exist a correlation between Number of Photos and Vehicle Price
Ha = There does exist a correlation between Number of Photos and Vehicle Price
We will use 0.05 as our significance level. The result of the correlation test was the following:
#getting the cor value
cor.test(cars_edited$number_of_photos, cars_edited$price_usd)##
## Pearson's product-moment correlation
##
## data: cars_edited$number_of_photos and cars_edited$price_usd
## t = 65.382, df = 38488, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3071525 0.3251358
## sample estimates:
## cor
## 0.3161726Since the p-value is less than 0.05 we can conclude that Price and Number of Photos are significantly correlated with a correlation coefficient of 0.3161726 and p-value of < 2.2e-16
Next we aim to investigate how good of an indicator of price the number of vehicle photos is. We shall employ linear regression, check the percentage of accuracy of that line, and graph the linear regression line to provide us with a useful visual.
#Getting the formula for linear regression
number_of_photos_on_price <- lm (price_usd ~ number_of_photos, data = cars_edited)
#Scatter plot: Number of photos and price with linear regression line
ggplot (cars_edited, aes(x=number_of_photos, y=price_usd)) + geom_point() + stat_smooth(method=lm)## `geom_smooth()` using formula 'y ~ x'
Although the linear regression model is created and graphed there remains information and insights to be gleaned. We proceed to check the R2 to see the proportion of the prices that can be explained by the model, the variability of the beta coefficients, and the percentage error.
summary(number_of_photos_on_price)##
## Call:
## lm(formula = price_usd ~ number_of_photos, data = cars_edited)
##
## Residuals:
## Min 1Q Median 3Q Max
## -24082 -3884 -1585 2249 44082
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3418.275 58.159 58.77 <2e-16 ***
## number_of_photos 333.297 5.098 65.38 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6095 on 38488 degrees of freedom
## Multiple R-squared: 0.09997, Adjusted R-squared: 0.09994
## F-statistic: 4275 on 1 and 38488 DF, p-value: < 2.2e-16# R^2 is very low(0.09994) which tells us that number of photos is not a good indicator of price.
# We can suspect that several more variables are in play.
confint(number_of_photos_on_price)## 2.5 % 97.5 %
## (Intercept) 3304.283 3532.2680
## number_of_photos 323.305 343.2882# 2.5 % 97.5 %
#(Intercept) 3304.283 3532.2680
#number_of_photos 323.305 343.2882
sigma(number_of_photos_on_price)*100/mean(cars_edited$price_usd)## [1] 91.88472# Our prediction error rate is extremely high (91.82279%) which explains the low correlationThe R2 is 0.09994 which indicates that a low proportion of prices in the data can be explained by the model. In other words, number of photos is not a good indicator of price(more attributes are needed in a model). Also, the percentage error is 91.88472 which is very high. This confirms how poor a model would be if it solely used number of photos to predict price.
We can conclude there is a low negative correlation between price and odometer. Our linear regression model tells us that to create an accurate model we will need to consider more attributes.
Q5: Does the number of times a vehicle has been upped in the catalog to raise its position impact the selling price?
A: The number of times a vehicle has been upped has a negligible impact on the selling price.
Justification:
Graphs used: Scatter plot How these graphs helped us solve the problem: Using a scatter plot for this question again allowed us to see the relationship between the two variables. With that we can come up with a solution to the question.
To begin answering this questions it was natural to use scatterplot (using 2 continuous attributes) to gain an intuitive understanding of any possible relationship.
#Regression analysis
ggplot(cars_edited, aes( x =up_counter, y=price_usd)) + geom_hex() + stat_smooth(color = "red")## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Looking at the Scatterplot we see a possible positive correlation although the variability of the data makes it hard for us to affirm this prediction. To continue we will use the correlation test(since we are dealing with continuous attributes) to check for a correlation between number of up counts and price of a vehicle.
The hypotheses for the correlation test are the following:
H0 =There does not exist a correlation between number of up counts and vehicle price
Ha = There does exist a correlation between number of up counts and vehicle price
We will use 0.05 as our significance level. The results are the following:
#Correlation
cor.test(cars_edited$up_counter, cars_edited$price_usd)##
## Pearson's product-moment correlation
##
## data: cars_edited$up_counter and cars_edited$price_usd
## t = 11.352, df = 38488, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.04780740 0.06772124
## sample estimates:
## cor
## 0.05777007Since the p-value is less than 0.05 we can conclude that Price and number of up counts are correlated with a correlation coefficient of 0.05777007 and p-value of < 2.2e-16.
However, although they are correlated we do see that the correlation coefficient is very close to 0. Meaning that the correlation is almost entirely negligible. If one were to attempt to use this as a predictor of price the results would be poor.
Nevertheless,we will use linear regression to see how poor of an indicator number of up counts really is. We will check the percentage of accuracy of the linear regression line, and graph the linear regression line to provide useful insights.
# Create LM
up_counter_on_price <- lm (price_usd ~ up_counter, data = cars_edited)
#Finding how well this line fits the data
summary(up_counter_on_price)##
## Call:
## lm(formula = price_usd ~ up_counter, data = cars_edited)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14558 -4502 -1852 2305 43438
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6493.0457 34.9345 185.86 <2e-16 ***
## up_counter 8.5694 0.7548 11.35 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6413 on 38488 degrees of freedom
## Multiple R-squared: 0.003337, Adjusted R-squared: 0.003311
## F-statistic: 128.9 on 1 and 38488 DF, p-value: < 2.2e-16# R^2 is extremely low which affirms that number of photos is not a good indicator of price.
confint(up_counter_on_price)## 2.5 % 97.5 %
## (Intercept) 6424.573138 6561.51822
## up_counter 7.089888 10.04893sigma(up_counter_on_price)*100/mean(cars_edited$price_usd)## [1] 96.69137# Our prediction error rate is extremely high (96.65168%) which confirms to us that up_counter is a terrible predictor of price(as we can see by the correlation test)
#Scatter plot: up counter and price with regression line
ggplot (cars_edited, aes(x=up_counter, y=price_usd)) + geom_point() + stat_smooth(method=lm)## `geom_smooth()` using formula 'y ~ x'
The R2 is 0.003311 which indicates that a low proportion of prices in the data can be explained by the model. In other words, number of up counts is a very poor indicator of price(we need more attributes in the model). Also, the percentage error is 96.69137 which is ludicrously high. This confirms how poor a model would be if it solely used number of up counts to predict price.
We can conclude there is a negligible positive correlation between price and number of up counts.
Q6: Relationship between Engine Type and Body Type? What is the impact of Engine Type and Body Type on the selling price?
A: Sedan and Gasoline is the most common followed by Gasoline and Hatchback. Engine Type and Body Type do have an impact on the selling price, but the extend of this impact will need to be investigated in our overall model.
Justification:
To begin this question it was important to have a working knowledge of how body type and engine type related to one another. Initially a Mosaic plot felt like the right graph to show relationships, but the quantity of categories made a balloon plot much easy to read. The spacious nature of the balloon plot greatly aided in gaining insights from the data and pushed further investigation.
# Balloon Plot
ggplot(cars_edited, aes(body_type, engine_type)) + geom_count()
Based on this Balloon plot we can see that several combinations of body types and engine types do not exist. Furthermore, there seems to be higher quantities of hatchbacks with gasoline engines and sedans with gasoline engines. Nevertheless, visualization does not suffice in proving any relationship. We shall proceed with a Chi-Square test for more information. The reason for a Chi-Square test is that we are attempting to analyze the frequency table of two categorical variables(engine type and body type).
The hypotheses for the Chi-Square test are as follows:
H0 = Engine Type and Body Type are independent
Ha = Engine Type and Body Type are dependent
The test will be performed with 0.05 as our significance level. The results are the following:
#Chi-Square Test
engine_body.data <- table(cars_edited$body_type, cars_edited$engine_type)
chisq.test(engine_body.data)## Warning in chisq.test(engine_body.data): Chi-squared approximation may be
## incorrect##
## Pearson's Chi-squared test
##
## data: engine_body.data
## X-squared = 6965.4, df = 22, p-value < 2.2e-16The result of the Chi-Square Test tells us that Engine Type and Body Type are dependent.
We continue with a proportion table to give us an easy way of seeing the distribution of engine type and body type.
# Table
prop.table(engine_body.data)*100##
## diesel electric gasoline
## cabriolet 0.010392310 0.000000000 0.184463497
## coupe 0.080540400 0.000000000 1.613406080
## hatchback 4.081579631 0.020784619 15.757339569
## liftback 0.249415433 0.005196155 1.176929072
## limousine 0.000000000 0.000000000 0.031176929
## minibus 3.273577553 0.000000000 0.280592362
## minivan 5.081839439 0.000000000 4.292023902
## pickup 0.192257729 0.000000000 0.142894258
## sedan 6.635489738 0.000000000 27.079760977
## suv 4.359573915 0.000000000 9.051701741
## universal 7.622759158 0.000000000 6.677058976
## van 1.847233048 0.000000000 0.252013510From the table we can clearly see that Hatchback and Gasoline as well as Sedan and Gasoline are the most common combinations of the two variables as we suspected from the balloon plot.
We continue by attempting to solve that second part of the question. That is, what is the impact of Engine Type and Body Type on the selling price?
The most effective method of solving this question is with the use of the Two-Way Anova. The reason for the use of this test is because we are attempting to evaluate the simultaneous effect of two grouping variables(engine and body type) on a response variable(price).
The hypotheses for the Two-Way Anova test are:
Ho = 1. There is no difference in the means of Engine Type 2. There is no difference in the means of Body Type. 3. There is no interaction between engine and body type
Ha = For cases 1 and 2 the means are not equal and for case 3 there is an interaction between Engine and Body Type.
body_engine_type_on_price.aov <- aov(price_usd ~ engine_type * body_type, data = cars_edited)
summary(body_engine_type_on_price.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## engine_type 2 1.301e+10 6.503e+09 204.12 <2e-16 ***
## body_type 11 3.457e+11 3.142e+10 986.32 <2e-16 ***
## engine_type:body_type 11 4.280e+09 3.891e+08 12.21 <2e-16 ***
## Residuals 38465 1.225e+12 3.186e+07
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Since the p-value is less than 0.05 for all three cases we can confirm that there is a difference in the means of Engine Type, the means of Body Type, and there is an interaction between Engine and Body Type.
To conclude our investigation we will run the Tukey Honest Significant Differences in order to perform the multiple pairwise-comparisons between the means of groups to determine which groups are different and how they differ.
#Tukey HSD
TukeyHSD(body_engine_type_on_price.aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = price_usd ~ engine_type * body_type, data = cars_edited)
##
## $engine_type
## diff lwr upr p adj
## electric-diesel 10052.532 5867.612 14237.452 1e-07
## gasoline-diesel -1175.359 -1318.298 -1032.420 0e+00
## gasoline-electric -11227.891 -15412.002 -7043.779 0e+00
##
## $body_type
## diff lwr upr p adj
## coupe-cabriolet -3510.93890 -5760.05406 -1261.8237 0.0000218
## hatchback-cabriolet -7129.82230 -9270.19038 -4989.4542 0.0000000
## liftback-cabriolet -3285.65559 -5555.93678 -1015.3744 0.0001423
## limousine-cabriolet -2758.76153 -8493.81258 2976.2895 0.9193030
## minibus-cabriolet -3529.16912 -5716.72120 -1341.6170 0.0000088
## minivan-cabriolet -5419.24172 -7571.21007 -3267.2734 0.0000000
## pickup-cabriolet 161.12611 -2517.35454 2839.6068 1.0000000
## sedan-cabriolet -5362.19106 -7498.28165 -3226.1005 0.0000000
## suv-cabriolet 2473.04299 327.68105 4618.4049 0.0090611
## universal-cabriolet -6522.64572 -8667.05290 -4378.2385 0.0000000
## van-cabriolet -5272.55651 -7499.16037 -3045.9527 0.0000000
## hatchback-coupe -3618.88340 -4371.45730 -2866.3095 0.0000000
## liftback-coupe 225.28331 -842.12881 1292.6954 0.9999350
## limousine-coupe 752.17737 -4621.46222 6125.8170 0.9999991
## minibus-coupe -18.23022 -896.05483 859.5944 1.0000000
## minivan-coupe -1908.30282 -2693.26124 -1123.3444 0.0000000
## pickup-coupe 3672.06502 1894.58069 5449.5493 0.0000000
## sedan-coupe -1851.25216 -2591.57298 -1110.9313 0.0000000
## suv-coupe 5983.98190 5217.32043 6750.6434 0.0000000
## universal-coupe -3011.70681 -3775.69248 -2247.7211 0.0000000
## van-coupe -1761.61761 -2732.67717 -790.5581 0.0000002
## liftback-hatchback 3844.16671 3030.51741 4657.8160 0.0000000
## limousine-hatchback 4371.06077 -957.97845 9700.1000 0.2358264
## minibus-hatchback 3600.65318 3059.14363 4142.1627 0.0000000
## minivan-hatchback 1710.58058 1338.00004 2083.1611 0.0000000
## pickup-hatchback 7290.94841 5653.23443 8928.6624 0.0000000
## sedan-hatchback 1767.63124 1501.67738 2033.5851 0.0000000
## suv-hatchback 9602.86529 9270.56071 9935.1699 0.0000000
## universal-hatchback 607.17658 281.09279 933.2604 0.0000001
## van-hatchback 1857.26579 1174.90723 2539.6243 0.0000000
## limousine-liftback 526.89406 -4855.63882 5909.4269 1.0000000
## minibus-liftback -243.51353 -1174.23034 687.2033 0.9994686
## minivan-liftback -2133.58613 -2977.27887 -1289.8934 0.0000000
## pickup-liftback 3446.78170 1642.58984 5250.9736 0.0000000
## sedan-liftback -2076.53548 -2878.86498 -1274.2060 0.0000000
## suv-liftback 5758.69858 4932.00183 6585.3953 0.0000000
## universal-liftback -3236.99013 -4061.20601 -2412.7742 0.0000000
## van-liftback -1986.90092 -3006.02524 -967.7766 0.0000000
## minibus-limousine -770.40759 -6118.57248 4577.7573 0.9999987
## minivan-limousine -2660.48019 -7994.18915 2673.2288 0.8982890
## pickup-limousine 2919.88764 -2647.13509 8486.9104 0.8622214
## sedan-limousine -2603.42954 -7930.75217 2723.8931 0.9106371
## suv-limousine 5231.80452 -99.24241 10562.8515 0.0601517
## universal-limousine -3763.88419 -9094.54697 1566.7786 0.4710753
## van-limousine -2513.79498 -7878.05153 2850.4616 0.9321174
## minivan-minibus -1890.07260 -2475.75583 -1304.3894 0.0000000
## pickup-minibus 3690.29524 1991.37921 5389.2113 0.0000000
## sedan-minibus -1833.02194 -2357.36919 -1308.6747 0.0000000
## suv-minibus 6002.21211 5441.28884 6563.1354 0.0000000
## universal-minibus -2993.47660 -3550.73706 -2436.2161 0.0000000
## van-minibus -1743.38739 -2561.81380 -924.9610 0.0000000
## pickup-minivan 5580.36783 3927.52200 7233.2137 0.0000000
## sedan-minivan 57.05065 -290.11459 404.2159 0.9999950
## suv-minivan 7892.28471 7492.01157 8292.5579 0.0000000
## universal-minivan -1103.40400 -1498.52789 -708.2801 0.0000000
## van-minivan 146.68521 -571.23224 864.6026 0.9999530
## sedan-pickup -5523.31718 -7155.43682 -3891.1975 0.0000000
## suv-pickup 2311.91688 667.68168 3956.1521 0.0002708
## universal-pickup -6683.77183 -8326.76109 -5040.7826 0.0000000
## van-pickup -5433.68263 -7182.59551 -3684.7697 0.0000000
## suv-sedan 7835.23406 7531.69860 8138.7695 0.0000000
## universal-sedan -1160.45465 -1457.16677 -863.7425 0.0000000
## van-sedan 89.63455 -579.18578 758.4549 0.9999994
## universal-suv -8995.68871 -9353.08620 -8638.2912 0.0000000
## van-suv -7745.59951 -8443.46448 -7047.7345 0.0000000
## van-universal 1250.08920 555.16487 1945.0135 0.0000003
##
## $`engine_type:body_type`
## diff lwr upr
## electric:cabriolet-diesel:cabriolet NA NA NA
## gasoline:cabriolet-diesel:cabriolet 4886.12394 -6244.21419 16016.462082
## diesel:coupe-diesel:cabriolet 3770.43194 -7736.51410 15277.377968
## electric:coupe-diesel:cabriolet NA NA NA
## gasoline:coupe-diesel:cabriolet 974.87185 -9889.41214 11839.155845
## diesel:hatchback-diesel:cabriolet -2230.26043 -13073.50085 8612.980002
## electric:hatchback-diesel:cabriolet 8862.50000 -4400.82863 22125.828633
## gasoline:hatchback-diesel:cabriolet -2350.01229 -13183.04532 8483.020736
## diesel:liftback-diesel:cabriolet 1889.10417 -9163.66969 12941.878028
## electric:liftback-diesel:cabriolet 20124.50000 1367.32076 38881.679235
## gasoline:liftback-diesel:cabriolet 1362.95479 -9514.21480 12240.124385
## diesel:limousine-diesel:cabriolet NA NA NA
## electric:limousine-diesel:cabriolet NA NA NA
## gasoline:limousine-diesel:cabriolet 1804.08333 -10700.70282 14308.869490
## diesel:minibus-diesel:cabriolet 2288.02738 -8558.61110 13134.665864
## electric:minibus-diesel:cabriolet NA NA NA
## gasoline:minibus-diesel:cabriolet 112.09306 -10916.09173 11140.277845
## diesel:minivan-diesel:cabriolet 546.59630 -10293.93360 11387.126194
## electric:minivan-diesel:cabriolet NA NA NA
## gasoline:minivan-diesel:cabriolet -1125.92030 -11968.48558 9716.644987
## diesel:pickup-diesel:cabriolet 4353.90041 -6764.39842 15472.199234
## electric:pickup-diesel:cabriolet NA NA NA
## gasoline:pickup-diesel:cabriolet 6803.27600 -4413.07424 18019.626241
## diesel:sedan-diesel:cabriolet -236.46869 -11074.40825 10601.470864
## electric:sedan-diesel:cabriolet NA NA NA
## gasoline:sedan-diesel:cabriolet -649.26683 -11480.80710 10182.273447
## diesel:suv-diesel:cabriolet 8608.89306 -2233.46932 19451.255444
## electric:suv-diesel:cabriolet NA NA NA
## gasoline:suv-diesel:cabriolet 6844.36931 -3991.30807 17680.046692
## diesel:universal-diesel:cabriolet -565.78852 -11402.63054 10271.053489
## electric:universal-diesel:cabriolet NA NA NA
## gasoline:universal-diesel:cabriolet -2209.42317 -13047.30997 8628.463634
## diesel:van-diesel:cabriolet 592.28395 -10267.59843 11452.166331
## electric:van-diesel:cabriolet NA NA NA
## gasoline:van-diesel:cabriolet -1637.94608 -12688.44079 9412.548621
## gasoline:cabriolet-electric:cabriolet NA NA NA
## diesel:coupe-electric:cabriolet NA NA NA
## electric:coupe-electric:cabriolet NA NA NA
## gasoline:coupe-electric:cabriolet NA NA NA
## diesel:hatchback-electric:cabriolet NA NA NA
## electric:hatchback-electric:cabriolet NA NA NA
## gasoline:hatchback-electric:cabriolet NA NA NA
## diesel:liftback-electric:cabriolet NA NA NA
## electric:liftback-electric:cabriolet NA NA NA
## gasoline:liftback-electric:cabriolet NA NA NA
## diesel:limousine-electric:cabriolet NA NA NA
## electric:limousine-electric:cabriolet NA NA NA
## gasoline:limousine-electric:cabriolet NA NA NA
## diesel:minibus-electric:cabriolet NA NA NA
## electric:minibus-electric:cabriolet NA NA NA
## gasoline:minibus-electric:cabriolet NA NA NA
## diesel:minivan-electric:cabriolet NA NA NA
## electric:minivan-electric:cabriolet NA NA NA
## gasoline:minivan-electric:cabriolet NA NA NA
## diesel:pickup-electric:cabriolet NA NA NA
## electric:pickup-electric:cabriolet NA NA NA
## gasoline:pickup-electric:cabriolet NA NA NA
## diesel:sedan-electric:cabriolet NA NA NA
## electric:sedan-electric:cabriolet NA NA NA
## gasoline:sedan-electric:cabriolet NA NA NA
## diesel:suv-electric:cabriolet NA NA NA
## electric:suv-electric:cabriolet NA NA NA
## gasoline:suv-electric:cabriolet NA NA NA
## diesel:universal-electric:cabriolet NA NA NA
## electric:universal-electric:cabriolet NA NA NA
## gasoline:universal-electric:cabriolet NA NA NA
## diesel:van-electric:cabriolet NA NA NA
## electric:van-electric:cabriolet NA NA NA
## gasoline:van-electric:cabriolet NA NA NA
## diesel:coupe-gasoline:cabriolet -1115.69201 -5778.27915 3546.895129
## electric:coupe-gasoline:cabriolet NA NA NA
## gasoline:coupe-gasoline:cabriolet -3911.25209 -6624.65927 -1197.844914
## diesel:hatchback-gasoline:cabriolet -7116.38437 -9744.26846 -4488.500276
## electric:hatchback-gasoline:cabriolet 3976.37606 -4101.11107 12053.863181
## gasoline:hatchback-gasoline:cabriolet -7236.13623 -9821.57940 -4650.693069
## diesel:liftback-gasoline:cabriolet -2997.01978 -6387.25898 393.219425
## electric:liftback-gasoline:cabriolet 15238.37606 -291.00528 30767.757393
## gasoline:liftback-gasoline:cabriolet -3523.16915 -6287.71798 -758.620326
## diesel:limousine-gasoline:cabriolet NA NA NA
## electric:limousine-gasoline:cabriolet NA NA NA
## gasoline:limousine-gasoline:cabriolet -3082.04061 -9842.18770 3678.106478
## diesel:minibus-gasoline:cabriolet -2598.09656 -5239.96677 43.773646
## electric:minibus-gasoline:cabriolet NA NA NA
## gasoline:minibus-gasoline:cabriolet -4774.03089 -8083.22589 -1464.835884
## diesel:minivan-gasoline:cabriolet -4339.52765 -6956.20497 -1722.850318
## electric:minivan-gasoline:cabriolet NA NA NA
## gasoline:minivan-gasoline:cabriolet -6012.04424 -8637.14114 -3386.947338
## diesel:pickup-gasoline:cabriolet -532.22354 -4130.34536 3065.898287
## electric:pickup-gasoline:cabriolet NA NA NA
## gasoline:pickup-gasoline:cabriolet 1917.15206 -1973.40376 5807.707871
## diesel:sedan-gasoline:cabriolet -5122.59264 -7728.51774 -2516.667532
## electric:sedan-gasoline:cabriolet NA NA NA
## gasoline:sedan-gasoline:cabriolet -5535.39077 -8114.57214 -2956.209401
## diesel:suv-gasoline:cabriolet 3722.76912 1098.51040 6347.027838
## electric:suv-gasoline:cabriolet NA NA NA
## gasoline:suv-gasoline:cabriolet 1958.24537 -638.25538 4554.746113
## diesel:universal-gasoline:cabriolet -5451.91247 -8053.26916 -2850.555776
## electric:universal-gasoline:cabriolet NA NA NA
## gasoline:universal-gasoline:cabriolet -7095.54711 -9701.25280 -4489.841421
## diesel:van-gasoline:cabriolet -4293.83999 -6989.56941 -1598.110571
## electric:van-gasoline:cabriolet NA NA NA
## gasoline:van-gasoline:cabriolet -6524.07003 -9906.87138 -3141.268670
## electric:coupe-diesel:coupe NA NA NA
## gasoline:coupe-diesel:coupe -2795.56008 -6781.53028 1190.410117
## diesel:hatchback-diesel:coupe -6000.69236 -9928.94321 -2072.441512
## electric:hatchback-diesel:coupe 5092.06806 -3496.94691 13681.083036
## gasoline:hatchback-diesel:coupe -6120.44423 -10020.43093 -2220.457523
## diesel:liftback-diesel:coupe -1881.32777 -6355.59886 2592.943319
## electric:liftback-diesel:coupe 16354.06806 552.57875 32155.557381
## gasoline:liftback-diesel:coupe -2407.47715 -6428.43600 1613.481714
## diesel:limousine-diesel:coupe NA NA NA
## electric:limousine-diesel:coupe NA NA NA
## gasoline:limousine-diesel:coupe -1966.34860 -9330.10909 5397.411890
## diesel:minibus-diesel:coupe -1482.40455 -5420.02543 2455.216317
## electric:minibus-diesel:coupe NA NA NA
## gasoline:minibus-diesel:coupe -3658.33888 -8071.51818 754.840417
## diesel:minivan-diesel:coupe -3223.83564 -7144.59834 696.927065
## electric:minivan-diesel:coupe NA NA NA
## gasoline:minivan-diesel:coupe -4896.35223 -8822.73908 -969.965385
## diesel:pickup-diesel:coupe 583.46847 -4050.30543 5217.242374
## electric:pickup-diesel:coupe NA NA NA
## gasoline:pickup-diesel:coupe 3032.84406 -1831.49602 7897.184150
## diesel:sedan-diesel:coupe -4006.90063 -7920.49560 -93.305659
## electric:sedan-diesel:coupe NA NA NA
## gasoline:sedan-diesel:coupe -4419.69876 -8315.53712 -523.860411
## diesel:suv-diesel:coupe 4838.46113 912.63462 8764.287633
## electric:suv-diesel:coupe NA NA NA
## gasoline:suv-diesel:coupe 3073.93738 -833.38857 6981.263322
## diesel:universal-diesel:coupe -4336.22046 -8246.77497 -425.665951
## electric:universal-diesel:coupe NA NA NA
## gasoline:universal-diesel:coupe -5979.85510 -9893.30397 -2066.406232
## diesel:van-diesel:coupe -3178.14798 -7152.10534 795.809369
## electric:van-diesel:coupe NA NA NA
## gasoline:van-diesel:coupe -5408.37802 -9877.01595 -939.740089
## gasoline:coupe-electric:coupe NA NA NA
## diesel:hatchback-electric:coupe NA NA NA
## electric:hatchback-electric:coupe NA NA NA
## gasoline:hatchback-electric:coupe NA NA NA
## diesel:liftback-electric:coupe NA NA NA
## electric:liftback-electric:coupe NA NA NA
## gasoline:liftback-electric:coupe NA NA NA
## diesel:limousine-electric:coupe NA NA NA
## electric:limousine-electric:coupe NA NA NA
## gasoline:limousine-electric:coupe NA NA NA
## diesel:minibus-electric:coupe NA NA NA
## electric:minibus-electric:coupe NA NA NA
## gasoline:minibus-electric:coupe NA NA NA
## diesel:minivan-electric:coupe NA NA NA
## electric:minivan-electric:coupe NA NA NA
## gasoline:minivan-electric:coupe NA NA NA
## diesel:pickup-electric:coupe NA NA NA
## electric:pickup-electric:coupe NA NA NA
## gasoline:pickup-electric:coupe NA NA NA
## diesel:sedan-electric:coupe NA NA NA
## electric:sedan-electric:coupe NA NA NA
## gasoline:sedan-electric:coupe NA NA NA
## diesel:suv-electric:coupe NA NA NA
## electric:suv-electric:coupe NA NA NA
## gasoline:suv-electric:coupe NA NA NA
## diesel:universal-electric:coupe NA NA NA
## electric:universal-electric:coupe NA NA NA
## gasoline:universal-electric:coupe NA NA NA
## diesel:van-electric:coupe NA NA NA
## electric:van-electric:coupe NA NA NA
## gasoline:van-electric:coupe NA NA NA
## diesel:hatchback-gasoline:coupe -3205.13228 -4231.78433 -2178.480228
## electric:hatchback-gasoline:coupe 7887.62815 180.87540 15594.380894
## gasoline:hatchback-gasoline:coupe -3324.88414 -4237.43879 -2412.329497
## diesel:liftback-gasoline:coupe 914.23231 -1461.04903 3289.513660
## electric:liftback-gasoline:coupe 19149.62815 3809.81315 34489.443151
## gasoline:liftback-gasoline:coupe 388.08294 -950.18827 1726.354152
## diesel:limousine-gasoline:coupe NA NA NA
## electric:limousine-gasoline:coupe NA NA NA
## gasoline:limousine-gasoline:coupe 829.21148 -5483.30214 7141.725107
## diesel:minibus-gasoline:coupe 1313.15553 251.21494 2375.096121
## electric:minibus-gasoline:coupe NA NA NA
## gasoline:minibus-gasoline:coupe -862.77880 -3120.87782 1395.320227
## diesel:minivan-gasoline:coupe -428.27555 -1425.89264 569.341537
## electric:minivan-gasoline:coupe NA NA NA
## gasoline:minivan-gasoline:coupe -2100.79215 -3120.28877 -1081.295522
## diesel:pickup-gasoline:coupe 3379.02855 715.43694 6042.620168
## electric:pickup-gasoline:coupe NA NA NA
## gasoline:pickup-gasoline:coupe 5828.40415 2781.32946 8875.478838
## diesel:sedan-gasoline:coupe -1211.34054 -2180.40467 -242.276422
## electric:sedan-gasoline:coupe NA NA NA
## gasoline:sedan-gasoline:coupe -1624.13868 -2518.79849 -729.478873
## diesel:suv-gasoline:coupe 7634.02121 6616.68476 8651.357659
## electric:suv-gasoline:coupe NA NA NA
## gasoline:suv-gasoline:coupe 5869.49746 4926.06980 6812.925117
## diesel:universal-gasoline:coupe -1540.66038 -2497.37155 -583.949207
## electric:universal-gasoline:coupe NA NA NA
## gasoline:universal-gasoline:coupe -3184.29502 -4152.76895 -2215.821084
## diesel:van-gasoline:coupe -382.58790 -1572.20832 807.032516
## electric:van-gasoline:coupe NA NA NA
## gasoline:van-gasoline:coupe -2612.81793 -4977.47111 -248.164759
## electric:hatchback-diesel:hatchback 11092.76043 3415.70147 18769.819380
## gasoline:hatchback-diesel:hatchback -119.75186 -732.90134 493.397612
## diesel:liftback-diesel:hatchback 4119.36459 1842.27044 6396.458743
## electric:liftback-diesel:hatchback 22354.76043 7029.84214 37679.678717
## gasoline:liftback-diesel:hatchback 3593.21522 2438.15412 4748.276315
## diesel:limousine-diesel:hatchback NA NA NA
## electric:limousine-diesel:hatchback NA NA NA
## gasoline:limousine-diesel:hatchback 4034.34376 -2241.88318 10310.570698
## diesel:minibus-diesel:hatchback 4518.28781 3699.19433 5337.381282
## electric:minibus-diesel:hatchback NA NA NA
## gasoline:minibus-diesel:hatchback 2342.35348 187.77522 4496.931747
## diesel:minivan-diesel:hatchback 2776.85673 2043.07463 3510.638817
## electric:minivan-diesel:hatchback NA NA NA
## gasoline:minivan-diesel:hatchback 1104.34013 341.07761 1867.602647
## diesel:pickup-diesel:hatchback 6584.16083 4007.74538 9160.576285
## electric:pickup-diesel:hatchback NA NA NA
## gasoline:pickup-diesel:hatchback 9033.53643 6062.36486 12004.707991
## diesel:sedan-diesel:hatchback 1993.79173 1299.32678 2688.256688
## electric:sedan-diesel:hatchback NA NA NA
## gasoline:sedan-diesel:hatchback 1580.99360 994.80893 2167.178270
## diesel:suv-diesel:hatchback 10839.15349 10078.77875 11599.528231
## electric:suv-diesel:hatchback NA NA NA
## gasoline:suv-diesel:hatchback 9074.62974 8416.41089 9732.848581
## diesel:universal-diesel:hatchback 1664.47190 987.35111 2341.592690
## electric:universal-diesel:hatchback NA NA NA
## gasoline:universal-diesel:hatchback 20.83726 -672.80390 714.478422
## diesel:van-diesel:hatchback 2822.54438 1843.56888 3801.519880
## electric:van-diesel:hatchback NA NA NA
## gasoline:van-diesel:hatchback 592.31434 -1673.69116 2858.319847
## gasoline:hatchback-electric:hatchback -11212.51229 -18875.14733 -3549.877252
## diesel:liftback-electric:hatchback -6973.39583 -14943.66441 996.872745
## electric:liftback-electric:hatchback 11262.00000 -5860.88364 28384.883637
## gasoline:liftback-electric:hatchback -7499.54521 -15224.45230 225.361883
## diesel:limousine-electric:hatchback NA NA NA
## electric:limousine-electric:hatchback NA NA NA
## gasoline:limousine-electric:hatchback -7058.41667 -16944.31814 2827.484811
## diesel:minibus-electric:hatchback -6574.47262 -14256.33031 1107.385071
## electric:minibus-electric:hatchback NA NA NA
## gasoline:minibus-electric:hatchback -8750.40694 -16686.54145 -814.272435
## diesel:minivan-electric:hatchback -8315.90370 -15989.13376 -642.673638
## electric:minivan-electric:hatchback NA NA NA
## gasoline:minivan-electric:hatchback -9988.42030 -17664.52563 -2312.314962
## diesel:pickup-electric:hatchback -4508.59959 -12569.48913 3552.289937
## electric:pickup-electric:hatchback NA NA NA
## gasoline:pickup-electric:hatchback -2059.22400 -10254.82550 6136.377498
## diesel:sedan-electric:hatchback -9098.96869 -16768.53876 -1429.398625
## electric:sedan-electric:hatchback NA NA NA
## gasoline:sedan-electric:hatchback -9511.76683 -17172.29135 -1851.242309
## diesel:suv-electric:hatchback -253.60694 -7929.42567 7422.211794
## electric:suv-electric:hatchback NA NA NA
## gasoline:suv-electric:hatchback -2018.13069 -9684.50372 5648.242345
## diesel:universal-electric:hatchback -9428.28852 -17096.30756 -1760.269484
## electric:universal-electric:hatchback NA NA NA
## gasoline:universal-electric:hatchback -11071.92317 -18741.41869 -3402.427649
## diesel:van-electric:hatchback -8270.21605 -15970.76255 -569.669543
## electric:van-electric:hatchback NA NA NA
## gasoline:van-electric:hatchback -10500.44608 -18467.55374 -2533.338428
## diesel:liftback-gasoline:hatchback 4239.11646 2011.13546 6467.097452
## electric:liftback-gasoline:hatchback 22474.51229 7156.81461 37792.209968
## gasoline:liftback-gasoline:hatchback 3712.96708 2658.02268 4767.911481
## diesel:limousine-gasoline:hatchback NA NA NA
## electric:limousine-gasoline:hatchback NA NA NA
## gasoline:limousine-gasoline:hatchback 4154.09562 -2104.47978 10412.671030
## diesel:minibus-gasoline:hatchback 4638.03967 3967.47598 5308.603365
## electric:minibus-gasoline:hatchback NA NA NA
## gasoline:minibus-gasoline:hatchback 2462.10535 359.50005 4564.710646
## diesel:minivan-gasoline:hatchback 2896.60859 2333.42327 3459.793910
## electric:minivan-gasoline:hatchback NA NA NA
## gasoline:minivan-gasoline:hatchback 1224.09199 623.00031 1825.183682
## diesel:pickup-gasoline:hatchback 6703.91270 4170.80035 9237.025037
## electric:pickup-gasoline:hatchback NA NA NA
## gasoline:pickup-gasoline:hatchback 9153.28829 6219.58721 12086.989368
## diesel:sedan-gasoline:hatchback 2113.54360 1602.63938 2624.447816
## electric:sedan-gasoline:hatchback NA NA NA
## gasoline:sedan-gasoline:hatchback 1700.74546 1350.95407 2050.536851
## diesel:suv-gasoline:hatchback 10958.90535 10361.48482 11556.325887
## electric:suv-gasoline:hatchback NA NA NA
## gasoline:suv-gasoline:hatchback 9194.38160 8733.95398 9654.809221
## diesel:universal-gasoline:hatchback 1784.22377 1297.15692 2271.290608
## electric:universal-gasoline:hatchback NA NA NA
## gasoline:universal-gasoline:hatchback 140.58912 -369.19476 650.373009
## diesel:van-gasoline:hatchback 2942.29624 2083.73082 3800.861662
## electric:van-gasoline:hatchback NA NA NA
## gasoline:van-gasoline:hatchback 712.06621 -1504.58047 2928.712884
## electric:liftback-diesel:liftback 18235.39583 2761.51243 33709.279239
## gasoline:liftback-diesel:liftback -526.14938 -2959.68860 1907.389852
## diesel:limousine-diesel:liftback NA NA NA
## electric:limousine-diesel:liftback NA NA NA
## gasoline:limousine-diesel:liftback -85.02083 -6716.68515 6546.643483
## diesel:minibus-diesel:liftback 398.92321 -1894.29748 2692.143909
## electric:minibus-diesel:liftback NA NA NA
## gasoline:minibus-diesel:liftback -1777.01111 -4815.12805 1261.105828
## diesel:minivan-diesel:liftback -1342.50787 -3606.65963 921.643892
## electric:minivan-diesel:liftback NA NA NA
## gasoline:minivan-diesel:liftback -3015.02446 -5288.90148 -741.147443
## diesel:pickup-diesel:liftback 2464.79624 -885.70576 5815.298235
## electric:pickup-diesel:liftback NA NA NA
## gasoline:pickup-diesel:liftback 4914.17183 1251.41229 8576.931381
## diesel:sedan-diesel:liftback -2125.57286 -4377.28967 126.143954
## electric:sedan-diesel:liftback NA NA NA
## gasoline:sedan-diesel:liftback -2538.37099 -4759.08248 -317.659509
## diesel:suv-diesel:liftback 6719.78890 4446.87958 8992.698218
## electric:suv-diesel:liftback NA NA NA
## gasoline:suv-diesel:liftback 4955.26514 2714.46192 7196.068367
## diesel:universal-diesel:liftback -2454.89269 -4701.32088 -208.464505
## electric:universal-diesel:liftback NA NA NA
## gasoline:universal-diesel:liftback -4098.52733 -6349.99021 -1847.064455
## diesel:van-diesel:liftback -1296.82021 -3651.88711 1058.246678
## electric:van-diesel:liftback NA NA NA
## gasoline:van-diesel:liftback -3527.05025 -6645.17917 -408.921327
## gasoline:liftback-electric:liftback -18761.54521 -34110.48902 -3412.601402
## diesel:limousine-electric:liftback NA NA NA
## electric:limousine-electric:liftback NA NA NA
## gasoline:limousine-electric:liftback -18320.41667 -34862.69385 -1778.139482
## diesel:minibus-electric:liftback -17836.47262 -33163.79541 -2509.149826
## electric:minibus-electric:liftback NA NA NA
## gasoline:minibus-electric:liftback -20012.40694 -35468.73631 -4556.077581
## diesel:minivan-electric:liftback -19577.90370 -34900.90426 -4254.903145
## electric:minivan-electric:liftback NA NA NA
## gasoline:minivan-electric:liftback -21250.42030 -36574.86089 -5925.979702
## diesel:pickup-electric:liftback -15770.59959 -31291.35429 -249.844894
## electric:pickup-electric:liftback NA NA NA
## gasoline:pickup-electric:liftback -13321.22400 -28912.36797 2269.919972
## diesel:sedan-electric:liftback -20360.96869 -35682.13678 -5039.800608
## electric:sedan-electric:liftback NA NA NA
## gasoline:sedan-electric:liftback -20773.76683 -36090.40883 -5457.124822
## diesel:suv-electric:liftback -11515.60694 -26839.90397 3808.690098
## electric:suv-electric:liftback NA NA NA
## gasoline:suv-electric:liftback -13280.13069 -28599.69863 2039.437253
## diesel:universal-electric:liftback -20690.28852 -36010.68024 -5369.896805
## electric:universal-electric:liftback NA NA NA
## gasoline:universal-electric:liftback -22333.92317 -37655.05393 -7012.792402
## diesel:van-electric:liftback -19532.21605 -34868.91396 -4195.518134
## electric:van-electric:liftback NA NA NA
## gasoline:van-electric:liftback -21762.44608 -37234.70160 -6290.190564
## diesel:limousine-gasoline:liftback NA NA NA
## electric:limousine-gasoline:liftback NA NA NA
## gasoline:limousine-gasoline:liftback 441.12854 -5893.53640 6775.793489
## diesel:minibus-gasoline:liftback 925.07259 -261.46419 2111.609370
## electric:minibus-gasoline:liftback NA NA NA
## gasoline:minibus-gasoline:liftback -1250.86173 -3570.16398 1068.440511
## diesel:minivan-gasoline:liftback -816.35849 -1945.69085 312.973863
## electric:minivan-gasoline:liftback NA NA NA
## gasoline:minivan-gasoline:liftback -2488.87509 -3637.58091 -1340.169260
## diesel:pickup-gasoline:liftback 2990.94562 275.27406 5706.617170
## electric:pickup-gasoline:liftback NA NA NA
## gasoline:pickup-gasoline:liftback 5440.32121 2347.61756 8533.024861
## diesel:sedan-gasoline:liftback -1599.42348 -2703.61413 -495.232838
## electric:sedan-gasoline:liftback NA NA NA
## gasoline:sedan-gasoline:liftback -2012.22162 -3051.72529 -972.717950
## diesel:suv-gasoline:liftback 7245.93827 6099.14921 8392.727336
## electric:suv-gasoline:liftback NA NA NA
## gasoline:suv-gasoline:liftback 5481.41452 4399.65325 6563.175794
## diesel:universal-gasoline:liftback -1928.74331 -3022.10875 -835.377882
## electric:universal-gasoline:liftback NA NA NA
## gasoline:universal-gasoline:liftback -3572.37796 -4676.05068 -2468.705239
## diesel:van-gasoline:liftback -770.67084 -2072.72631 531.384629
## electric:van-gasoline:liftback NA NA NA
## gasoline:van-gasoline:liftback -3000.90087 -5424.06747 -577.734278
## electric:limousine-diesel:limousine NA NA NA
## gasoline:limousine-diesel:limousine NA NA NA
## diesel:minibus-diesel:limousine NA NA NA
## electric:minibus-diesel:limousine NA NA NA
## gasoline:minibus-diesel:limousine NA NA NA
## diesel:minivan-diesel:limousine NA NA NA
## electric:minivan-diesel:limousine NA NA NA
## gasoline:minivan-diesel:limousine NA NA NA
## diesel:pickup-diesel:limousine NA NA NA
## electric:pickup-diesel:limousine NA NA NA
## gasoline:pickup-diesel:limousine NA NA NA
## diesel:sedan-diesel:limousine NA NA NA
## electric:sedan-diesel:limousine NA NA NA
## gasoline:sedan-diesel:limousine NA NA NA
## diesel:suv-diesel:limousine NA NA NA
## electric:suv-diesel:limousine NA NA NA
## gasoline:suv-diesel:limousine NA NA NA
## diesel:universal-diesel:limousine NA NA NA
## electric:universal-diesel:limousine NA NA NA
## gasoline:universal-diesel:limousine NA NA NA
## diesel:van-diesel:limousine NA NA NA
## electric:van-diesel:limousine NA NA NA
## gasoline:van-diesel:limousine NA NA NA
## gasoline:limousine-electric:limousine NA NA NA
## diesel:minibus-electric:limousine NA NA NA
## electric:minibus-electric:limousine NA NA NA
## gasoline:minibus-electric:limousine NA NA NA
## diesel:minivan-electric:limousine NA NA NA
## electric:minivan-electric:limousine NA NA NA
## gasoline:minivan-electric:limousine NA NA NA
## diesel:pickup-electric:limousine NA NA NA
## electric:pickup-electric:limousine NA NA NA
## gasoline:pickup-electric:limousine NA NA NA
## diesel:sedan-electric:limousine NA NA NA
## electric:sedan-electric:limousine NA NA NA
## gasoline:sedan-electric:limousine NA NA NA
## diesel:suv-electric:limousine NA NA NA
## electric:suv-electric:limousine NA NA NA
## gasoline:suv-electric:limousine NA NA NA
## diesel:universal-electric:limousine NA NA NA
## electric:universal-electric:limousine NA NA NA
## gasoline:universal-electric:limousine NA NA NA
## diesel:van-electric:limousine NA NA NA
## electric:van-electric:limousine NA NA NA
## gasoline:van-electric:limousine NA NA NA
## diesel:minibus-gasoline:limousine 483.94405 -5798.15178 6766.039873
## electric:minibus-gasoline:limousine NA NA NA
## gasoline:minibus-gasoline:limousine -1691.99028 -8282.59126 4898.610707
## diesel:minivan-gasoline:limousine -1257.48703 -7529.02991 5014.055838
## electric:minivan-gasoline:limousine NA NA NA
## gasoline:minivan-gasoline:limousine -2930.00363 -9205.06407 3345.056809
## diesel:pickup-gasoline:limousine 2549.81707 -4190.48933 9290.123477
## electric:pickup-gasoline:limousine NA NA NA
## gasoline:pickup-gasoline:limousine 4999.19267 -1901.65325 11900.038579
## diesel:sedan-gasoline:limousine -2040.55203 -8307.61636 4226.512312
## electric:sedan-gasoline:limousine NA NA NA
## gasoline:sedan-gasoline:limousine -2453.35016 -8709.34139 3802.641072
## diesel:suv-gasoline:limousine 6804.80973 530.09989 13079.519571
## electric:suv-gasoline:limousine NA NA NA
## gasoline:suv-gasoline:limousine 5040.28598 -1222.86546 11303.437412
## diesel:universal-gasoline:limousine -2369.87186 -8635.03797 3895.294254
## electric:universal-gasoline:limousine NA NA NA
## gasoline:universal-gasoline:limousine -4013.50650 -10280.47961 2253.466605
## diesel:van-gasoline:limousine -1211.79938 -7516.73450 5093.135739
## electric:van-gasoline:limousine NA NA NA
## gasoline:van-gasoline:limousine -3442.02942 -10069.89444 3185.835608
## electric:minibus-diesel:minibus NA NA NA
## gasoline:minibus-diesel:minibus -2175.93433 -4347.54914 -4.319514
## diesel:minivan-diesel:minibus -1741.43108 -2523.82456 -959.037609
## electric:minivan-diesel:minibus NA NA NA
## gasoline:minivan-diesel:minibus -3413.94768 -4224.05450 -2603.840858
## diesel:pickup-diesel:minibus 2065.87302 -524.80642 4656.552468
## electric:pickup-diesel:minibus NA NA NA
## gasoline:pickup-diesel:minibus 4515.24862 1531.69975 7498.797488
## diesel:sedan-diesel:minibus -2524.49607 -3270.14006 -1778.852083
## electric:sedan-diesel:minibus NA NA NA
## gasoline:sedan-diesel:minibus -2937.29421 -3583.29410 -2291.294320
## diesel:suv-diesel:minibus 6320.86568 5513.47907 7128.252290
## electric:suv-diesel:minibus NA NA NA
## gasoline:suv-diesel:minibus 4556.34193 3844.33390 5268.349959
## diesel:universal-diesel:minibus -2853.81591 -3583.33352 -2124.298291
## electric:universal-diesel:minibus NA NA NA
## gasoline:universal-diesel:minibus -4497.45055 -5242.32735 -3752.573746
## diesel:van-diesel:minibus -1695.74343 -2711.66489 -679.821965
## electric:van-diesel:minibus NA NA NA
## gasoline:van-diesel:minibus -3925.97346 -6208.18387 -1643.763060
## gasoline:minibus-electric:minibus NA NA NA
## diesel:minivan-electric:minibus NA NA NA
## electric:minivan-electric:minibus NA NA NA
## gasoline:minivan-electric:minibus NA NA NA
## diesel:pickup-electric:minibus NA NA NA
## electric:pickup-electric:minibus NA NA NA
## gasoline:pickup-electric:minibus NA NA NA
## diesel:sedan-electric:minibus NA NA NA
## electric:sedan-electric:minibus NA NA NA
## gasoline:sedan-electric:minibus NA NA NA
## diesel:suv-electric:minibus NA NA NA
## electric:suv-electric:minibus NA NA NA
## gasoline:suv-electric:minibus NA NA NA
## diesel:universal-electric:minibus NA NA NA
## electric:universal-electric:minibus NA NA NA
## gasoline:universal-electric:minibus NA NA NA
## diesel:van-electric:minibus NA NA NA
## electric:van-electric:minibus NA NA NA
## gasoline:van-electric:minibus NA NA NA
## diesel:minivan-gasoline:minibus 434.50324 -1706.39211 2575.398599
## electric:minivan-gasoline:minibus NA NA NA
## gasoline:minivan-gasoline:minibus -1238.01335 -3389.19127 913.164567
## diesel:pickup-gasoline:minibus 4241.80735 973.33472 7510.279984
## electric:pickup-gasoline:minibus NA NA NA
## gasoline:pickup-gasoline:minibus 6691.18294 3103.30652 10279.059368
## diesel:sedan-gasoline:minibus -348.56175 -2476.30194 1779.178448
## electric:sedan-gasoline:minibus NA NA NA
## gasoline:sedan-gasoline:minibus -761.35988 -2856.26065 1333.540886
## diesel:suv-gasoline:minibus 8496.80001 6346.64501 10646.955005
## electric:suv-gasoline:minibus NA NA NA
## gasoline:suv-gasoline:minibus 6732.27626 4616.08893 8848.463585
## diesel:universal-gasoline:minibus -677.88158 -2800.02421 1444.261047
## electric:universal-gasoline:minibus NA NA NA
## gasoline:universal-gasoline:minibus -2321.51622 -4448.98769 -194.044759
## diesel:van-gasoline:minibus 480.19090 -1756.63493 2717.016728
## electric:van-gasoline:minibus NA NA NA
## gasoline:van-gasoline:minibus -1750.03914 -4779.85394 1279.775659
## electric:minivan-diesel:minivan NA NA NA
## gasoline:minivan-diesel:minivan -1672.51660 -2396.25349 -948.779698
## diesel:pickup-diesel:minivan 3807.30411 1242.32028 6372.287930
## electric:pickup-diesel:minivan NA NA NA
## gasoline:pickup-diesel:minivan 6256.67970 3295.41546 9217.943940
## diesel:sedan-diesel:minivan -783.06499 -1433.83914 -132.290837
## electric:sedan-diesel:minivan NA NA NA
## gasoline:sedan-diesel:minivan -1195.86313 -1729.56519 -662.161061
## diesel:suv-diesel:minivan 8062.29676 7341.60600 8782.987526
## electric:suv-diesel:minivan NA NA NA
## gasoline:suv-diesel:minivan 6297.77301 5685.82737 6909.718657
## diesel:universal-diesel:minivan -1112.38482 -1744.61737 -480.152280
## electric:universal-diesel:minivan NA NA NA
## gasoline:universal-diesel:minivan -2756.01947 -3405.91445 -2106.124483
## diesel:van-diesel:minivan 45.68765 -902.79453 994.169840
## electric:van-diesel:minivan NA NA NA
## gasoline:van-diesel:minivan -2184.54238 -4437.54180 68.457034
## gasoline:minivan-electric:minivan NA NA NA
## diesel:pickup-electric:minivan NA NA NA
## electric:pickup-electric:minivan NA NA NA
## gasoline:pickup-electric:minivan NA NA NA
## diesel:sedan-electric:minivan NA NA NA
## electric:sedan-electric:minivan NA NA NA
## gasoline:sedan-electric:minivan NA NA NA
## diesel:suv-electric:minivan NA NA NA
## electric:suv-electric:minivan NA NA NA
## gasoline:suv-electric:minivan NA NA NA
## diesel:universal-electric:minivan NA NA NA
## electric:universal-electric:minivan NA NA NA
## gasoline:universal-electric:minivan NA NA NA
## diesel:van-electric:minivan NA NA NA
## electric:van-electric:minivan NA NA NA
## gasoline:van-electric:minivan NA NA NA
## diesel:pickup-gasoline:minivan 5479.82070 2906.24818 8053.393224
## electric:pickup-gasoline:minivan NA NA NA
## gasoline:pickup-gasoline:minivan 7929.19630 4960.48961 10897.902985
## diesel:sedan-gasoline:minivan 889.45160 205.60914 1573.294065
## electric:sedan-gasoline:minivan NA NA NA
## gasoline:sedan-gasoline:minivan 476.65347 -96.90682 1050.213759
## diesel:suv-gasoline:minivan 9734.81336 8984.12788 10485.498836
## electric:suv-gasoline:minivan NA NA NA
## gasoline:suv-gasoline:minivan 7970.28961 7323.28807 8617.291141
## diesel:universal-gasoline:minivan 560.13177 -106.09004 1226.353582
## electric:universal-gasoline:minivan NA NA NA
## gasoline:universal-gasoline:minivan -1083.50287 -1766.50873 -400.497014
## diesel:van-gasoline:minivan 1718.20425 746.73527 2689.673224
## electric:van-gasoline:minivan NA NA NA
## gasoline:van-gasoline:minivan -512.02579 -2774.79839 1750.746822
## electric:pickup-diesel:pickup NA NA NA
## gasoline:pickup-diesel:pickup 2449.37559 -1406.60240 6305.353593
## diesel:sedan-diesel:pickup -4590.36910 -7144.38308 -2036.355114
## electric:sedan-diesel:pickup NA NA NA
## gasoline:sedan-diesel:pickup -5003.16723 -7529.88810 -2476.446369
## diesel:suv-diesel:pickup 4254.99266 1682.27511 6827.710210
## electric:suv-diesel:pickup NA NA NA
## gasoline:suv-diesel:pickup 2490.46891 -53.92845 5034.866261
## diesel:universal-diesel:pickup -4919.68893 -7469.04148 -2370.336381
## electric:universal-diesel:pickup NA NA NA
## gasoline:universal-diesel:pickup -6563.32357 -9117.11368 -4009.533464
## diesel:van-diesel:pickup -3761.61645 -6407.19746 -1116.035442
## electric:van-diesel:pickup NA NA NA
## gasoline:van-diesel:pickup -5991.84649 -9334.82223 -2648.870748
## gasoline:pickup-electric:pickup NA NA NA
## diesel:sedan-electric:pickup NA NA NA
## electric:sedan-electric:pickup NA NA NA
## gasoline:sedan-electric:pickup NA NA NA
## diesel:suv-electric:pickup NA NA NA
## electric:suv-electric:pickup NA NA NA
## gasoline:suv-electric:pickup NA NA NA
## diesel:universal-electric:pickup NA NA NA
## electric:universal-electric:pickup NA NA NA
## gasoline:universal-electric:pickup NA NA NA
## diesel:van-electric:pickup NA NA NA
## electric:van-electric:pickup NA NA NA
## gasoline:van-electric:pickup NA NA NA
## diesel:sedan-gasoline:pickup -7039.74469 -9991.51218 -4087.977204
## electric:sedan-gasoline:pickup NA NA NA
## gasoline:sedan-gasoline:pickup -7452.54283 -10380.72694 -4524.358715
## diesel:suv-gasoline:pickup 1805.61706 -1162.34848 4773.582609
## electric:suv-gasoline:pickup NA NA NA
## gasoline:suv-gasoline:pickup 41.09331 -2902.35735 2984.543970
## diesel:universal-gasoline:pickup -7369.06452 -10316.79964 -4421.329411
## electric:universal-gasoline:pickup NA NA NA
## gasoline:universal-gasoline:pickup -9012.69917 -11964.27295 -6061.125384
## diesel:van-gasoline:pickup -6210.99205 -9242.33544 -3179.648655
## electric:van-gasoline:pickup NA NA NA
## gasoline:van-gasoline:pickup -8441.22208 -12097.09827 -4785.345899
## electric:sedan-diesel:sedan NA NA NA
## gasoline:sedan-diesel:sedan -412.79814 -891.00657 65.410301
## diesel:suv-diesel:sedan 8845.36176 8164.74396 9525.979553
## electric:suv-diesel:sedan NA NA NA
## gasoline:suv-diesel:sedan 7080.83800 6516.63690 7645.039107
## diesel:universal-diesel:sedan -329.31983 -915.46265 256.822984
## electric:universal-diesel:sedan NA NA NA
## gasoline:universal-diesel:sedan -1972.95448 -2578.10644 -1367.802508
## diesel:van-diesel:sedan 828.75264 -89.65025 1747.155536
## electric:van-diesel:sedan NA NA NA
## gasoline:van-diesel:sedan -1401.47739 -3641.97996 839.025182
## gasoline:sedan-electric:sedan NA NA NA
## diesel:suv-electric:sedan NA NA NA
## electric:suv-electric:sedan NA NA NA
## gasoline:suv-electric:sedan NA NA NA
## diesel:universal-electric:sedan NA NA NA
## electric:universal-electric:sedan NA NA NA
## gasoline:universal-electric:sedan NA NA NA
## diesel:van-electric:sedan NA NA NA
## electric:van-electric:sedan NA NA NA
## gasoline:van-electric:sedan NA NA NA
## diesel:suv-gasoline:sedan 9258.15989 8688.44814 9827.871646
## electric:suv-gasoline:sedan NA NA NA
## gasoline:suv-gasoline:sedan 7493.63614 7069.78029 7917.491990
## diesel:universal-gasoline:sedan 83.47830 -369.17421 536.130817
## electric:universal-gasoline:sedan NA NA NA
## gasoline:universal-gasoline:sedan -1560.15634 -2037.16766 -1083.145019
## diesel:van-gasoline:sedan 1241.55078 402.03022 2081.071339
## electric:van-gasoline:sedan NA NA NA
## gasoline:van-gasoline:sedan -988.67925 -3198.01913 1220.660619
## electric:suv-diesel:suv NA NA NA
## gasoline:suv-diesel:suv -1764.52375 -2408.11606 -1120.931443
## diesel:universal-diesel:suv -9174.68159 -9837.59303 -8511.770149
## electric:universal-diesel:suv NA NA NA
## gasoline:universal-diesel:suv -10818.31623 -11498.09346 -10138.539004
## diesel:van-diesel:suv -8016.60911 -8985.81087 -7047.407354
## electric:van-diesel:suv NA NA NA
## gasoline:van-diesel:suv -10246.83915 -12508.63930 -7985.038988
## gasoline:suv-electric:suv NA NA NA
## diesel:universal-electric:suv NA NA NA
## electric:universal-electric:suv NA NA NA
## gasoline:universal-electric:suv NA NA NA
## diesel:van-electric:suv NA NA NA
## electric:van-electric:suv NA NA NA
## gasoline:van-electric:suv NA NA NA
## diesel:universal-gasoline:suv -7410.15784 -7952.86759 -6867.448084
## electric:universal-gasoline:suv NA NA NA
## gasoline:universal-gasoline:suv -9053.79248 -9616.97928 -8490.605675
## diesel:van-gasoline:suv -6252.08536 -7143.39582 -5360.774894
## electric:van-gasoline:suv NA NA NA
## gasoline:van-gasoline:suv -8482.31539 -10711.84948 -6252.781305
## electric:universal-diesel:universal NA NA NA
## gasoline:universal-diesel:universal -1643.63464 -2228.80119 -1058.468093
## diesel:van-diesel:universal 1158.07248 252.71351 2063.431444
## electric:van-diesel:universal NA NA NA
## gasoline:van-diesel:universal -1072.15756 -3307.34497 1163.029854
## gasoline:universal-electric:universal NA NA NA
## diesel:van-electric:universal NA NA NA
## electric:van-electric:universal NA NA NA
## gasoline:van-electric:universal NA NA NA
## diesel:van-gasoline:universal 2801.70712 1883.92699 3719.487247
## electric:van-gasoline:universal NA NA NA
## gasoline:van-gasoline:universal 571.47708 -1668.77028 2811.724453
## electric:van-diesel:van NA NA NA
## gasoline:van-diesel:van -2230.23003 -4574.57712 114.117047
## gasoline:van-electric:van NA NA NA
## p adj
## electric:cabriolet-diesel:cabriolet NA
## gasoline:cabriolet-diesel:cabriolet 0.9996511
## diesel:coupe-diesel:cabriolet 0.9999996
## electric:coupe-diesel:cabriolet NA
## gasoline:coupe-diesel:cabriolet 1.0000000
## diesel:hatchback-diesel:cabriolet 1.0000000
## electric:hatchback-diesel:cabriolet 0.8154076
## gasoline:hatchback-diesel:cabriolet 1.0000000
## diesel:liftback-diesel:cabriolet 1.0000000
## electric:liftback-diesel:cabriolet 0.0178178
## gasoline:liftback-diesel:cabriolet 1.0000000
## diesel:limousine-diesel:cabriolet NA
## electric:limousine-diesel:cabriolet NA
## gasoline:limousine-diesel:cabriolet 1.0000000
## diesel:minibus-diesel:cabriolet 1.0000000
## electric:minibus-diesel:cabriolet NA
## gasoline:minibus-diesel:cabriolet 1.0000000
## diesel:minivan-diesel:cabriolet 1.0000000
## electric:minivan-diesel:cabriolet NA
## gasoline:minivan-diesel:cabriolet 1.0000000
## diesel:pickup-diesel:cabriolet 0.9999711
## electric:pickup-diesel:cabriolet NA
## gasoline:pickup-diesel:cabriolet 0.9321329
## diesel:sedan-diesel:cabriolet 1.0000000
## electric:sedan-diesel:cabriolet NA
## gasoline:sedan-diesel:cabriolet 1.0000000
## diesel:suv-diesel:cabriolet 0.4292644
## electric:suv-diesel:cabriolet NA
## gasoline:suv-diesel:cabriolet 0.8932795
## diesel:universal-diesel:cabriolet 1.0000000
## electric:universal-diesel:cabriolet NA
## gasoline:universal-diesel:cabriolet 1.0000000
## diesel:van-diesel:cabriolet 1.0000000
## electric:van-diesel:cabriolet NA
## gasoline:van-diesel:cabriolet 1.0000000
## gasoline:cabriolet-electric:cabriolet NA
## diesel:coupe-electric:cabriolet NA
## electric:coupe-electric:cabriolet NA
## gasoline:coupe-electric:cabriolet NA
## diesel:hatchback-electric:cabriolet NA
## electric:hatchback-electric:cabriolet NA
## gasoline:hatchback-electric:cabriolet NA
## diesel:liftback-electric:cabriolet NA
## electric:liftback-electric:cabriolet NA
## gasoline:liftback-electric:cabriolet NA
## diesel:limousine-electric:cabriolet NA
## electric:limousine-electric:cabriolet NA
## gasoline:limousine-electric:cabriolet NA
## diesel:minibus-electric:cabriolet NA
## electric:minibus-electric:cabriolet NA
## gasoline:minibus-electric:cabriolet NA
## diesel:minivan-electric:cabriolet NA
## electric:minivan-electric:cabriolet NA
## gasoline:minivan-electric:cabriolet NA
## diesel:pickup-electric:cabriolet NA
## electric:pickup-electric:cabriolet NA
## gasoline:pickup-electric:cabriolet NA
## diesel:sedan-electric:cabriolet NA
## electric:sedan-electric:cabriolet NA
## gasoline:sedan-electric:cabriolet NA
## diesel:suv-electric:cabriolet NA
## electric:suv-electric:cabriolet NA
## gasoline:suv-electric:cabriolet NA
## diesel:universal-electric:cabriolet NA
## electric:universal-electric:cabriolet NA
## gasoline:universal-electric:cabriolet NA
## diesel:van-electric:cabriolet NA
## electric:van-electric:cabriolet NA
## gasoline:van-electric:cabriolet NA
## diesel:coupe-gasoline:cabriolet 1.0000000
## electric:coupe-gasoline:cabriolet NA
## gasoline:coupe-gasoline:cabriolet 0.0000194
## diesel:hatchback-gasoline:cabriolet 0.0000000
## electric:hatchback-gasoline:cabriolet 0.9968849
## gasoline:hatchback-gasoline:cabriolet 0.0000000
## diesel:liftback-gasoline:cabriolet 0.1957844
## electric:liftback-gasoline:cabriolet 0.0638644
## gasoline:liftback-gasoline:cabriolet 0.0005776
## diesel:limousine-gasoline:cabriolet NA
## electric:limousine-gasoline:cabriolet NA
## gasoline:limousine-gasoline:cabriolet 0.9992550
## diesel:minibus-gasoline:cabriolet 0.0621080
## electric:minibus-gasoline:cabriolet NA
## gasoline:minibus-gasoline:cabriolet 0.0000189
## diesel:minivan-gasoline:cabriolet 0.0000001
## electric:minivan-gasoline:cabriolet NA
## gasoline:minivan-gasoline:cabriolet 0.0000000
## diesel:pickup-gasoline:cabriolet 1.0000000
## electric:pickup-gasoline:cabriolet NA
## gasoline:pickup-gasoline:cabriolet 0.9968296
## diesel:sedan-gasoline:cabriolet 0.0000000
## electric:sedan-gasoline:cabriolet NA
## gasoline:sedan-gasoline:cabriolet 0.0000000
## diesel:suv-gasoline:cabriolet 0.0000318
## electric:suv-gasoline:cabriolet NA
## gasoline:suv-gasoline:cabriolet 0.5575133
## diesel:universal-gasoline:cabriolet 0.0000000
## electric:universal-gasoline:cabriolet NA
## gasoline:universal-gasoline:cabriolet 0.0000000
## diesel:van-gasoline:cabriolet 0.0000006
## electric:van-gasoline:cabriolet NA
## gasoline:van-gasoline:cabriolet 0.0000000
## electric:coupe-diesel:coupe NA
## gasoline:coupe-diesel:coupe 0.7250355
## diesel:hatchback-diesel:coupe 0.0000028
## electric:hatchback-diesel:coupe 0.9485097
## gasoline:hatchback-diesel:coupe 0.0000011
## diesel:liftback-diesel:coupe 0.9998589
## electric:liftback-diesel:coupe 0.0309514
## gasoline:liftback-diesel:coupe 0.9418794
## diesel:limousine-diesel:coupe NA
## electric:limousine-diesel:coupe NA
## gasoline:limousine-diesel:coupe 1.0000000
## diesel:minibus-diesel:coupe 0.9999885
## electric:minibus-diesel:coupe NA
## gasoline:minibus-diesel:coupe 0.3264607
## diesel:minivan-diesel:coupe 0.3451627
## electric:minivan-diesel:coupe NA
## gasoline:minivan-diesel:coupe 0.0009626
## diesel:pickup-diesel:coupe 1.0000000
## electric:pickup-diesel:coupe NA
## gasoline:pickup-diesel:coupe 0.9072200
## diesel:sedan-diesel:coupe 0.0361690
## electric:sedan-diesel:coupe NA
## gasoline:sedan-diesel:coupe 0.0068017
## diesel:suv-diesel:coupe 0.0012564
## electric:suv-diesel:coupe NA
## gasoline:suv-diesel:coupe 0.4521546
## diesel:universal-diesel:coupe 0.0102522
## electric:universal-diesel:coupe NA
## gasoline:universal-diesel:coupe 0.0000028
## diesel:van-diesel:coupe 0.4115505
## electric:van-diesel:coupe NA
## gasoline:van-diesel:coupe 0.0018706
## gasoline:coupe-electric:coupe NA
## diesel:hatchback-electric:coupe NA
## electric:hatchback-electric:coupe NA
## gasoline:hatchback-electric:coupe NA
## diesel:liftback-electric:coupe NA
## electric:liftback-electric:coupe NA
## gasoline:liftback-electric:coupe NA
## diesel:limousine-electric:coupe NA
## electric:limousine-electric:coupe NA
## gasoline:limousine-electric:coupe NA
## diesel:minibus-electric:coupe NA
## electric:minibus-electric:coupe NA
## gasoline:minibus-electric:coupe NA
## diesel:minivan-electric:coupe NA
## electric:minivan-electric:coupe NA
## gasoline:minivan-electric:coupe NA
## diesel:pickup-electric:coupe NA
## electric:pickup-electric:coupe NA
## gasoline:pickup-electric:coupe NA
## diesel:sedan-electric:coupe NA
## electric:sedan-electric:coupe NA
## gasoline:sedan-electric:coupe NA
## diesel:suv-electric:coupe NA
## electric:suv-electric:coupe NA
## gasoline:suv-electric:coupe NA
## diesel:universal-electric:coupe NA
## electric:universal-electric:coupe NA
## gasoline:universal-electric:coupe NA
## diesel:van-electric:coupe NA
## electric:van-electric:coupe NA
## gasoline:van-electric:coupe NA
## diesel:hatchback-gasoline:coupe 0.0000000
## electric:hatchback-gasoline:coupe 0.0363558
## gasoline:hatchback-gasoline:coupe 0.0000000
## diesel:liftback-gasoline:coupe 0.9999806
## electric:liftback-gasoline:coupe 0.0009394
## gasoline:liftback-gasoline:coupe 1.0000000
## diesel:limousine-gasoline:coupe NA
## electric:limousine-gasoline:coupe NA
## gasoline:limousine-gasoline:coupe 1.0000000
## diesel:minibus-gasoline:coupe 0.0011662
## electric:minibus-gasoline:coupe NA
## gasoline:minibus-gasoline:coupe 0.9999837
## diesel:minivan-gasoline:coupe 0.9997806
## electric:minivan-gasoline:coupe NA
## gasoline:minivan-gasoline:coupe 0.0000000
## diesel:pickup-gasoline:coupe 0.0006444
## electric:pickup-gasoline:coupe NA
## gasoline:pickup-gasoline:coupe 0.0000000
## diesel:sedan-gasoline:coupe 0.0009112
## electric:sedan-gasoline:coupe NA
## gasoline:sedan-gasoline:coupe 0.0000000
## diesel:suv-gasoline:coupe 0.0000000
## electric:suv-gasoline:coupe NA
## gasoline:suv-gasoline:coupe 0.0000000
## diesel:universal-gasoline:coupe 0.0000004
## electric:universal-gasoline:coupe NA
## gasoline:universal-gasoline:coupe 0.0000000
## diesel:van-gasoline:coupe 0.9999998
## electric:van-gasoline:coupe NA
## gasoline:van-gasoline:coupe 0.0109006
## electric:hatchback-diesel:hatchback 0.0000180
## gasoline:hatchback-diesel:hatchback 1.0000000
## diesel:liftback-diesel:hatchback 0.0000000
## electric:liftback-diesel:hatchback 0.0000133
## gasoline:liftback-diesel:hatchback 0.0000000
## diesel:limousine-diesel:hatchback NA
## electric:limousine-diesel:hatchback NA
## gasoline:limousine-diesel:hatchback 0.8721900
## diesel:minibus-diesel:hatchback 0.0000000
## electric:minibus-diesel:hatchback NA
## gasoline:minibus-diesel:hatchback 0.0143589
## diesel:minivan-diesel:hatchback 0.0000000
## electric:minivan-diesel:hatchback NA
## gasoline:minivan-diesel:hatchback 0.0000173
## diesel:pickup-diesel:hatchback 0.0000000
## electric:pickup-diesel:hatchback NA
## gasoline:pickup-diesel:hatchback 0.0000000
## diesel:sedan-diesel:hatchback 0.0000000
## electric:sedan-diesel:hatchback NA
## gasoline:sedan-diesel:hatchback 0.0000000
## diesel:suv-diesel:hatchback 0.0000000
## electric:suv-diesel:hatchback NA
## gasoline:suv-diesel:hatchback 0.0000000
## diesel:universal-diesel:hatchback 0.0000000
## electric:universal-diesel:hatchback NA
## gasoline:universal-diesel:hatchback 1.0000000
## diesel:van-diesel:hatchback 0.0000000
## electric:van-diesel:hatchback NA
## gasoline:van-diesel:hatchback 1.0000000
## gasoline:hatchback-electric:hatchback 0.0000121
## diesel:liftback-electric:hatchback 0.2144133
## electric:liftback-electric:hatchback 0.8402707
## gasoline:liftback-electric:hatchback 0.0729080
## diesel:limousine-electric:hatchback NA
## electric:limousine-electric:hatchback NA
## gasoline:limousine-electric:hatchback 0.6867809
## diesel:minibus-electric:hatchback 0.2573685
## electric:minibus-electric:hatchback NA
## gasoline:minibus-electric:hatchback 0.0113079
## diesel:minivan-electric:hatchback 0.0151226
## electric:minivan-electric:hatchback NA
## gasoline:minivan-electric:hatchback 0.0003457
## diesel:pickup-electric:hatchback 0.9762396
## electric:pickup-electric:hatchback NA
## gasoline:pickup-electric:hatchback 1.0000000
## diesel:sedan-electric:hatchback 0.0028448
## electric:sedan-electric:hatchback NA
## gasoline:sedan-electric:hatchback 0.0010625
## diesel:suv-electric:hatchback 1.0000000
## electric:suv-electric:hatchback NA
## gasoline:suv-electric:hatchback 1.0000000
## diesel:universal-electric:hatchback 0.0013244
## electric:universal-electric:hatchback NA
## gasoline:universal-electric:hatchback 0.0000185
## diesel:van-electric:hatchback 0.0175310
## electric:van-electric:hatchback NA
## gasoline:van-electric:hatchback 0.0002494
## diesel:liftback-gasoline:hatchback 0.0000000
## electric:liftback-gasoline:hatchback 0.0000111
## gasoline:liftback-gasoline:hatchback 0.0000000
## diesel:limousine-gasoline:hatchback NA
## electric:limousine-gasoline:hatchback NA
## gasoline:limousine-gasoline:hatchback 0.8262069
## diesel:minibus-gasoline:hatchback 0.0000000
## electric:minibus-gasoline:hatchback NA
## gasoline:minibus-gasoline:hatchback 0.0037043
## diesel:minivan-gasoline:hatchback 0.0000000
## electric:minivan-gasoline:hatchback NA
## gasoline:minivan-gasoline:hatchback 0.0000000
## diesel:pickup-gasoline:hatchback 0.0000000
## electric:pickup-gasoline:hatchback NA
## gasoline:pickup-gasoline:hatchback 0.0000000
## diesel:sedan-gasoline:hatchback 0.0000000
## electric:sedan-gasoline:hatchback NA
## gasoline:sedan-gasoline:hatchback 0.0000000
## diesel:suv-gasoline:hatchback 0.0000000
## electric:suv-gasoline:hatchback NA
## gasoline:suv-gasoline:hatchback 0.0000000
## diesel:universal-gasoline:hatchback 0.0000000
## electric:universal-gasoline:hatchback NA
## gasoline:universal-gasoline:hatchback 1.0000000
## diesel:van-gasoline:hatchback 0.0000000
## electric:van-gasoline:hatchback NA
## gasoline:van-gasoline:hatchback 0.9999998
## electric:liftback-diesel:liftback 0.0032599
## gasoline:liftback-diesel:liftback 1.0000000
## diesel:limousine-diesel:liftback NA
## electric:limousine-diesel:liftback NA
## gasoline:limousine-diesel:liftback 1.0000000
## diesel:minibus-diesel:liftback 1.0000000
## electric:minibus-diesel:liftback NA
## gasoline:minibus-diesel:liftback 0.9565832
## diesel:minivan-diesel:liftback 0.9484205
## electric:minivan-diesel:liftback NA
## gasoline:minivan-diesel:liftback 0.0002131
## diesel:pickup-diesel:liftback 0.6180313
## electric:pickup-diesel:liftback NA
## gasoline:pickup-diesel:liftback 0.0001558
## diesel:sedan-diesel:liftback 0.1011808
## electric:sedan-diesel:liftback NA
## gasoline:sedan-diesel:liftback 0.0059109
## diesel:suv-diesel:liftback 0.0000000
## electric:suv-diesel:liftback NA
## gasoline:suv-diesel:liftback 0.0000000
## diesel:universal-diesel:liftback 0.0131664
## electric:universal-diesel:liftback NA
## gasoline:universal-diesel:liftback 0.0000000
## diesel:van-diesel:liftback 0.9810090
## electric:van-diesel:liftback NA
## gasoline:van-diesel:liftback 0.0071790
## gasoline:liftback-electric:liftback 0.0015088
## diesel:limousine-electric:liftback NA
## electric:limousine-electric:liftback NA
## gasoline:limousine-electric:liftback 0.0104740
## diesel:minibus-electric:liftback 0.0041898
## electric:minibus-electric:liftback NA
## gasoline:minibus-electric:liftback 0.0003917
## diesel:minivan-electric:liftback 0.0005429
## electric:minivan-electric:liftback NA
## gasoline:minivan-electric:liftback 0.0000621
## diesel:pickup-electric:liftback 0.0402408
## electric:pickup-electric:liftback NA
## gasoline:pickup-electric:liftback 0.2608062
## diesel:sedan-electric:liftback 0.0002008
## electric:sedan-electric:liftback NA
## gasoline:sedan-electric:liftback 0.0001160
## diesel:suv-electric:liftback 0.5664469
## electric:suv-electric:liftback NA
## gasoline:suv-electric:liftback 0.2319068
## diesel:universal-electric:liftback 0.0001304
## electric:universal-electric:liftback NA
## gasoline:universal-electric:liftback 0.0000136
## diesel:van-electric:liftback 0.0005869
## electric:van-electric:liftback NA
## gasoline:van-electric:liftback 0.0000410
## diesel:limousine-gasoline:liftback NA
## electric:limousine-gasoline:liftback NA
## gasoline:limousine-gasoline:liftback 1.0000000
## diesel:minibus-gasoline:liftback 0.4746845
## electric:minibus-gasoline:liftback NA
## gasoline:minibus-gasoline:liftback 0.9860558
## diesel:minivan-gasoline:liftback 0.6589921
## electric:minivan-gasoline:liftback NA
## gasoline:minivan-gasoline:liftback 0.0000000
## diesel:pickup-gasoline:liftback 0.0115286
## electric:pickup-gasoline:liftback NA
## gasoline:pickup-gasoline:liftback 0.0000000
## diesel:sedan-gasoline:liftback 0.0000167
## electric:sedan-gasoline:liftback NA
## gasoline:sedan-gasoline:liftback 0.0000000
## diesel:suv-gasoline:liftback 0.0000000
## electric:suv-gasoline:liftback NA
## gasoline:suv-gasoline:liftback 0.0000000
## diesel:universal-gasoline:liftback 0.0000000
## electric:universal-gasoline:liftback NA
## gasoline:universal-gasoline:liftback 0.0000000
## diesel:van-gasoline:liftback 0.9495495
## electric:van-gasoline:liftback NA
## gasoline:van-gasoline:liftback 0.0011274
## electric:limousine-diesel:limousine NA
## gasoline:limousine-diesel:limousine NA
## diesel:minibus-diesel:limousine NA
## electric:minibus-diesel:limousine NA
## gasoline:minibus-diesel:limousine NA
## diesel:minivan-diesel:limousine NA
## electric:minivan-diesel:limousine NA
## gasoline:minivan-diesel:limousine NA
## diesel:pickup-diesel:limousine NA
## electric:pickup-diesel:limousine NA
## gasoline:pickup-diesel:limousine NA
## diesel:sedan-diesel:limousine NA
## electric:sedan-diesel:limousine NA
## gasoline:sedan-diesel:limousine NA
## diesel:suv-diesel:limousine NA
## electric:suv-diesel:limousine NA
## gasoline:suv-diesel:limousine NA
## diesel:universal-diesel:limousine NA
## electric:universal-diesel:limousine NA
## gasoline:universal-diesel:limousine NA
## diesel:van-diesel:limousine NA
## electric:van-diesel:limousine NA
## gasoline:van-diesel:limousine NA
## gasoline:limousine-electric:limousine NA
## diesel:minibus-electric:limousine NA
## electric:minibus-electric:limousine NA
## gasoline:minibus-electric:limousine NA
## diesel:minivan-electric:limousine NA
## electric:minivan-electric:limousine NA
## gasoline:minivan-electric:limousine NA
## diesel:pickup-electric:limousine NA
## electric:pickup-electric:limousine NA
## gasoline:pickup-electric:limousine NA
## diesel:sedan-electric:limousine NA
## electric:sedan-electric:limousine NA
## gasoline:sedan-electric:limousine NA
## diesel:suv-electric:limousine NA
## electric:suv-electric:limousine NA
## gasoline:suv-electric:limousine NA
## diesel:universal-electric:limousine NA
## electric:universal-electric:limousine NA
## gasoline:universal-electric:limousine NA
## diesel:van-electric:limousine NA
## electric:van-electric:limousine NA
## gasoline:van-electric:limousine NA
## diesel:minibus-gasoline:limousine 1.0000000
## electric:minibus-gasoline:limousine NA
## gasoline:minibus-gasoline:limousine 1.0000000
## diesel:minivan-gasoline:limousine 1.0000000
## electric:minivan-gasoline:limousine NA
## gasoline:minivan-gasoline:limousine 0.9988186
## diesel:pickup-gasoline:limousine 0.9999871
## electric:pickup-gasoline:limousine NA
## gasoline:pickup-gasoline:limousine 0.6540351
## diesel:sedan-gasoline:limousine 0.9999997
## electric:sedan-gasoline:limousine NA
## gasoline:sedan-gasoline:limousine 0.9999701
## diesel:suv-gasoline:limousine 0.0149561
## electric:suv-gasoline:limousine NA
## gasoline:suv-gasoline:limousine 0.3963283
## diesel:universal-gasoline:limousine 0.9999871
## electric:universal-gasoline:limousine NA
## gasoline:universal-gasoline:limousine 0.8768847
## diesel:van-gasoline:limousine 1.0000000
## electric:van-gasoline:limousine NA
## gasoline:van-gasoline:limousine 0.9922921
## electric:minibus-diesel:minibus NA
## gasoline:minibus-diesel:minibus 0.0486929
## diesel:minivan-diesel:minibus 0.0000000
## electric:minivan-diesel:minibus NA
## gasoline:minivan-diesel:minibus 0.0000000
## diesel:pickup-diesel:minibus 0.4186742
## electric:pickup-diesel:minibus NA
## gasoline:pickup-diesel:minibus 0.0000039
## diesel:sedan-diesel:minibus 0.0000000
## electric:sedan-diesel:minibus NA
## gasoline:sedan-diesel:minibus 0.0000000
## diesel:suv-diesel:minibus 0.0000000
## electric:suv-diesel:minibus NA
## gasoline:suv-diesel:minibus 0.0000000
## diesel:universal-diesel:minibus 0.0000000
## electric:universal-diesel:minibus NA
## gasoline:universal-diesel:minibus 0.0000000
## diesel:van-diesel:minibus 0.0000001
## electric:van-diesel:minibus NA
## gasoline:van-diesel:minibus 0.0000000
## gasoline:minibus-electric:minibus NA
## diesel:minivan-electric:minibus NA
## electric:minivan-electric:minibus NA
## gasoline:minivan-electric:minibus NA
## diesel:pickup-electric:minibus NA
## electric:pickup-electric:minibus NA
## gasoline:pickup-electric:minibus NA
## diesel:sedan-electric:minibus NA
## electric:sedan-electric:minibus NA
## gasoline:sedan-electric:minibus NA
## diesel:suv-electric:minibus NA
## electric:suv-electric:minibus NA
## gasoline:suv-electric:minibus NA
## diesel:universal-electric:minibus NA
## electric:universal-electric:minibus NA
## gasoline:universal-electric:minibus NA
## diesel:van-electric:minibus NA
## electric:van-electric:minibus NA
## gasoline:van-electric:minibus NA
## diesel:minivan-gasoline:minibus 1.0000000
## electric:minivan-gasoline:minibus NA
## gasoline:minivan-gasoline:minibus 0.9648647
## diesel:pickup-gasoline:minibus 0.0003695
## electric:pickup-gasoline:minibus NA
## gasoline:pickup-gasoline:minibus 0.0000000
## diesel:sedan-gasoline:minibus 1.0000000
## electric:sedan-gasoline:minibus NA
## gasoline:sedan-gasoline:minibus 0.9999951
## diesel:suv-gasoline:minibus 0.0000000
## electric:suv-gasoline:minibus NA
## gasoline:suv-gasoline:minibus 0.0000000
## diesel:universal-gasoline:minibus 0.9999998
## electric:universal-gasoline:minibus NA
## gasoline:universal-gasoline:minibus 0.0134925
## diesel:van-gasoline:minibus 1.0000000
## electric:van-gasoline:minibus NA
## gasoline:van-gasoline:minibus 0.9631272
## electric:minivan-diesel:minivan NA
## gasoline:minivan-diesel:minivan 0.0000000
## diesel:pickup-diesel:minivan 0.0000076
## electric:pickup-diesel:minivan NA
## gasoline:pickup-diesel:minivan 0.0000000
## diesel:sedan-diesel:minivan 0.0021176
## electric:sedan-diesel:minivan NA
## gasoline:sedan-diesel:minivan 0.0000000
## diesel:suv-diesel:minivan 0.0000000
## electric:suv-diesel:minivan NA
## gasoline:suv-diesel:minivan 0.0000000
## diesel:universal-diesel:minivan 0.0000000
## electric:universal-diesel:minivan NA
## gasoline:universal-diesel:minivan 0.0000000
## diesel:van-diesel:minivan 1.0000000
## electric:van-diesel:minivan NA
## gasoline:van-diesel:minivan 0.0740243
## gasoline:minivan-electric:minivan NA
## diesel:pickup-electric:minivan NA
## electric:pickup-electric:minivan NA
## gasoline:pickup-electric:minivan NA
## diesel:sedan-electric:minivan NA
## electric:sedan-electric:minivan NA
## gasoline:sedan-electric:minivan NA
## diesel:suv-electric:minivan NA
## electric:suv-electric:minivan NA
## gasoline:suv-electric:minivan NA
## diesel:universal-electric:minivan NA
## electric:universal-electric:minivan NA
## gasoline:universal-electric:minivan NA
## diesel:van-electric:minivan NA
## electric:van-electric:minivan NA
## gasoline:van-electric:minivan NA
## diesel:pickup-gasoline:minivan 0.0000000
## electric:pickup-gasoline:minivan NA
## gasoline:pickup-gasoline:minivan 0.0000000
## diesel:sedan-gasoline:minivan 0.0003495
## electric:sedan-gasoline:minivan NA
## gasoline:sedan-gasoline:minivan 0.3207608
## diesel:suv-gasoline:minivan 0.0000000
## electric:suv-gasoline:minivan NA
## gasoline:suv-gasoline:minivan 0.0000000
## diesel:universal-gasoline:minivan 0.2949449
## electric:universal-gasoline:minivan NA
## gasoline:universal-gasoline:minivan 0.0000007
## diesel:van-gasoline:minivan 0.0000000
## electric:van-gasoline:minivan NA
## gasoline:van-gasoline:minivan 1.0000000
## electric:pickup-diesel:pickup NA
## gasoline:pickup-diesel:pickup 0.8867955
## diesel:sedan-diesel:pickup 0.0000000
## electric:sedan-diesel:pickup NA
## gasoline:sedan-diesel:pickup 0.0000000
## diesel:suv-diesel:pickup 0.0000001
## electric:suv-diesel:pickup NA
## gasoline:suv-diesel:pickup 0.0659031
## diesel:universal-diesel:pickup 0.0000000
## electric:universal-diesel:pickup NA
## gasoline:universal-diesel:pickup 0.0000000
## diesel:van-diesel:pickup 0.0000296
## electric:van-diesel:pickup NA
## gasoline:van-diesel:pickup 0.0000000
## gasoline:pickup-electric:pickup NA
## diesel:sedan-electric:pickup NA
## electric:sedan-electric:pickup NA
## gasoline:sedan-electric:pickup NA
## diesel:suv-electric:pickup NA
## electric:suv-electric:pickup NA
## gasoline:suv-electric:pickup NA
## diesel:universal-electric:pickup NA
## electric:universal-electric:pickup NA
## gasoline:universal-electric:pickup NA
## diesel:van-electric:pickup NA
## electric:van-electric:pickup NA
## gasoline:van-electric:pickup NA
## diesel:sedan-gasoline:pickup 0.0000000
## electric:sedan-gasoline:pickup NA
## gasoline:sedan-gasoline:pickup 0.0000000
## diesel:suv-gasoline:pickup 0.9297101
## electric:suv-gasoline:pickup NA
## gasoline:suv-gasoline:pickup 1.0000000
## diesel:universal-gasoline:pickup 0.0000000
## electric:universal-gasoline:pickup NA
## gasoline:universal-gasoline:pickup 0.0000000
## diesel:van-gasoline:pickup 0.0000000
## electric:van-gasoline:pickup NA
## gasoline:van-gasoline:pickup 0.0000000
## electric:sedan-diesel:sedan NA
## gasoline:sedan-diesel:sedan 0.2401545
## diesel:suv-diesel:sedan 0.0000000
## electric:suv-diesel:sedan NA
## gasoline:suv-diesel:sedan 0.0000000
## diesel:universal-diesel:sedan 0.9746867
## electric:universal-diesel:sedan NA
## gasoline:universal-diesel:sedan 0.0000000
## diesel:van-diesel:sedan 0.1617026
## electric:van-diesel:sedan NA
## gasoline:van-diesel:sedan 0.9038666
## gasoline:sedan-electric:sedan NA
## diesel:suv-electric:sedan NA
## electric:suv-electric:sedan NA
## gasoline:suv-electric:sedan NA
## diesel:universal-electric:sedan NA
## electric:universal-electric:sedan NA
## gasoline:universal-electric:sedan NA
## diesel:van-electric:sedan NA
## electric:van-electric:sedan NA
## gasoline:van-electric:sedan NA
## diesel:suv-gasoline:sedan 0.0000000
## electric:suv-gasoline:sedan NA
## gasoline:suv-gasoline:sedan 0.0000000
## diesel:universal-gasoline:sedan 1.0000000
## electric:universal-gasoline:sedan NA
## gasoline:universal-gasoline:sedan 0.0000000
## diesel:van-gasoline:sedan 0.0000085
## electric:van-gasoline:sedan NA
## gasoline:van-gasoline:sedan 0.9994851
## electric:suv-diesel:suv NA
## gasoline:suv-diesel:suv 0.0000000
## diesel:universal-diesel:suv 0.0000000
## electric:universal-diesel:suv NA
## gasoline:universal-diesel:suv 0.0000000
## diesel:van-diesel:suv 0.0000000
## electric:van-diesel:suv NA
## gasoline:van-diesel:suv 0.0000000
## gasoline:suv-electric:suv NA
## diesel:universal-electric:suv NA
## electric:universal-electric:suv NA
## gasoline:universal-electric:suv NA
## diesel:van-electric:suv NA
## electric:van-electric:suv NA
## gasoline:van-electric:suv NA
## diesel:universal-gasoline:suv 0.0000000
## electric:universal-gasoline:suv NA
## gasoline:universal-gasoline:suv 0.0000000
## diesel:van-gasoline:suv 0.0000000
## electric:van-gasoline:suv NA
## gasoline:van-gasoline:suv 0.0000000
## electric:universal-diesel:universal NA
## gasoline:universal-diesel:universal 0.0000000
## diesel:van-diesel:universal 0.0005282
## electric:van-diesel:universal NA
## gasoline:van-diesel:universal 0.9980468
## gasoline:universal-electric:universal NA
## diesel:van-electric:universal NA
## electric:van-electric:universal NA
## gasoline:van-electric:universal NA
## diesel:van-gasoline:universal 0.0000000
## electric:van-gasoline:universal NA
## gasoline:van-gasoline:universal 1.0000000
## electric:van-diesel:van NA
## gasoline:van-diesel:van 0.0926816
## gasoline:van-electric:van NAThe Tukey Test results can be evaluated to gain more detailed information on the relationship between price and Engine/Body Type.
These tests confirm our that Engine Type and Body Type significantly impact the selling price of a vehicle.
Q7: What is the most popular model and whether we can conclude that the popularity of a model has a direct impact on the price of a vehicle?
A: The most popular is the Passat. Furthermore, The popularity of a vehicle does seem to have an impact on the average_price of a vehicle. However, more attributes would be needed to predict price.
Justification:
The nature of the first part of the question informs us that we can figure out the most popular model without a graph. To find the most popular model we performed a count on every model and printed out the result.
# Finding out model popularity
models_counted <- cars_edited %>% count(model_name)
models_counted %>% arrange(desc(n))## # A tibble: 1,118 x 2
## model_name n
## <chr> <int>
## 1 Passat 1422
## 2 Astra 751
## 3 Golf 707
## 4 A6 687
## 5 Mondeo 636
## 6 Vectra 565
## 7 Laguna 548
## 8 A4 505
## 9 406 415
## 10 Omega 387
## # ... with 1,108 more rowsFrom the table it is clear that the most popular model is the Passat.
Now we go on to solve the next part of the question: Whether we can conclude that the popularity of a model has a direct impact on the price of a vehicle?
Since we are using a categorical factor and attempting to find its impact on a continuous response variable we will deploy the One-Way Anova Test.
The hypotheses for the One-Way Anova test are:
Ho = The means of the different models are the same
Ha = At least one sample mean is not equal to the others.
Also, the level of significance to be used with this test is 0.05.
We proceed by running the following code:
# Do an anova test to see if model name significantly impacts price of a vehicle
model_price <- aov(price_usd ~ model_name, data = cars_edited)
summary(model_price)## Df Sum Sq Mean Sq F value Pr(>F)
## model_name 1117 9.775e+11 875117268 53.54 <2e-16 ***
## Residuals 37372 6.109e+11 16346307
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Since the p-value is less than 0.05 we can conclude that the model name does have a significant impact on the price of a vehicle.
We continue our investigation by attempting to see how effective model name is in predicting the price of a vehicle. In order to do this we will use a Simple Linear Regression model as follows:
#Taking the linear regression
modelPrice <- lm (price_usd ~ model_name, data = cars_edited)
#Making sure the linear regression line matches the model
summary(modelPrice)##
## Call:
## lm(formula = price_usd ~ model_name, data = cars_edited)
##
## Residuals:
## Min 1Q Median 3Q Max
## -19700 -1985 -361 1082 43065
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16632.33 1429.44 11.636 < 2e-16 ***
## model_name<U+0410>22 1151.98 2737.16 0.421 0.673854
## model_name<U+041C>20 -10829.57 2183.50 -4.960 7.09e-07 ***
## model_name<U+041C>5 6242.42 2475.86 2.521 0.011696 *
## model_name100 -15073.08 1444.77 -10.433 < 2e-16 ***
## model_name1007 -12148.99 2183.50 -5.564 2.65e-08 ***
## model_name100NX -15757.51 2475.86 -6.364 1.98e-10 ***
## model_name106 -15094.47 1791.89 -8.424 < 2e-16 ***
## model_name107 -12299.46 1845.39 -6.665 2.68e-11 ***
## model_name11 -16207.33 3196.32 -5.071 3.98e-07 ***
## model_name110 -15557.33 3196.32 -4.867 1.14e-06 ***
## model_name111 -16078.76 2475.86 -6.494 8.45e-11 ***
## model_name1111 -14295.07 2475.86 -5.774 7.81e-09 ***
## model_name1119 -16132.33 4288.31 -3.762 0.000169 ***
## model_name112 -15541.56 4288.31 -3.624 0.000290 ***
## model_name116 -7051.65 1642.30 -4.294 1.76e-05 ***
## model_name118 -6368.99 1770.04 -3.598 0.000321 ***
## model_name12 -15114.74 4288.31 -3.525 0.000425 ***
## model_name120 -7187.33 1917.79 -3.748 0.000179 ***
## model_name121 -15932.47 2092.48 -7.614 2.72e-14 ***
## model_name125 -15982.33 4288.31 -3.727 0.000194 ***
## model_name1300 -15132.33 4288.31 -3.529 0.000418 ***
## model_name145 -15433.35 1791.89 -8.613 < 2e-16 ***
## model_name146 -15504.68 1634.62 -9.485 < 2e-16 ***
## model_name147 -13149.83 1845.39 -7.126 1.05e-12 ***
## model_name1500 6567.67 2475.86 2.653 0.007989 **
## model_name155 -15911.04 2092.48 -7.604 2.94e-14 ***
## model_name156 -14365.19 1489.65 -9.643 < 2e-16 ***
## model_name159 -8232.45 2021.53 -4.072 4.66e-05 ***
## model_name164 -15672.33 2304.90 -6.800 1.07e-11 ***
## model_name166 -13561.56 1614.61 -8.399 < 2e-16 ***
## model_name180 -14332.33 4288.31 -3.342 0.000832 ***
## model_name19 -16056.58 1486.07 -10.805 < 2e-16 ***
## model_name190 -15305.62 1516.15 -10.095 < 2e-16 ***
## model_name2 -11689.61 1791.89 -6.524 6.95e-11 ***
## model_name200 -15022.33 1917.79 -7.833 4.88e-15 ***
## model_name200-Series -15417.81 1537.47 -10.028 < 2e-16 ***
## model_name2008 -5353.14 2304.90 -2.323 0.020211 *
## model_name200SX -2952.53 2304.90 -1.281 0.200209
## model_name205 -16014.03 2304.90 -6.948 3.77e-12 ***
## model_name206 -13621.37 1469.71 -9.268 < 2e-16 ***
## model_name207 -11554.08 1504.72 -7.679 1.65e-14 ***
## model_name208 -8133.53 1917.79 -4.241 2.23e-05 ***
## model_name21 -12397.34 1541.72 -8.041 9.15e-16 ***
## model_name2101 -15547.10 1614.61 -9.629 < 2e-16 ***
## model_name21011 -15567.43 1917.79 -8.117 4.91e-16 ***
## model_name21013 -15968.79 2021.53 -7.899 2.88e-15 ***
## model_name2102 -15679.32 2183.50 -7.181 7.06e-13 ***
## model_name2103 -14397.03 1917.79 -7.507 6.18e-14 ***
## model_name2104 -15366.98 1816.78 -8.458 < 2e-16 ***
## model_name2105 -15685.61 1584.40 -9.900 < 2e-16 ***
## model_name2106 -15851.54 1492.30 -10.622 < 2e-16 ***
## model_name2107 -15669.40 1539.55 -10.178 < 2e-16 ***
## model_name2108 -15892.11 1593.31 -9.974 < 2e-16 ***
## model_name2109 -15815.56 1541.72 -10.258 < 2e-16 ***
## model_name21099 -15796.33 1770.04 -8.924 < 2e-16 ***
## model_name2110 -15457.40 1791.89 -8.626 < 2e-16 ***
## model_name2111 -15982.33 3196.32 -5.000 5.75e-07 ***
## model_name2112 -15753.61 2021.53 -7.793 6.72e-15 ***
## model_name2113 -14982.32 3196.32 -4.687 2.78e-06 ***
## model_name2114 -14922.22 1791.89 -8.328 < 2e-16 ***
## model_name2115 -15345.96 1878.65 -8.169 3.22e-16 ***
## model_name2120 -15432.33 4288.31 -3.599 0.000320 ***
## model_name2121 -13390.22 1614.61 -8.293 < 2e-16 ***
## model_name21213 -14071.33 1964.57 -7.163 8.07e-13 ***
## model_name21214 -12132.26 1917.79 -6.326 2.54e-10 ***
## model_name2123 -12632.33 4288.31 -2.946 0.003224 **
## model_name2125 -16414.83 3196.32 -5.136 2.83e-07 ***
## model_name2131 -13557.93 1917.79 -7.070 1.58e-12 ***
## model_name2140 -15974.39 1679.79 -9.510 < 2e-16 ***
## model_name2141 -15891.62 2183.50 -7.278 3.45e-13 ***
## model_name216 -1820.42 1878.65 -0.969 0.332550
## model_name218 431.01 2737.16 0.157 0.874879
## model_name22 -14732.33 4288.31 -3.435 0.000592 ***
## model_name220 6567.67 4288.31 1.532 0.125647
## model_name2206 -15632.32 4288.31 -3.645 0.000267 ***
## model_name225 -10682.33 3196.32 -3.342 0.000832 ***
## model_name235 15917.68 4288.31 3.712 0.000206 ***
## model_name24 -13178.53 1791.89 -7.355 1.96e-13 ***
## model_name240 -13642.63 2475.86 -5.510 3.61e-08 ***
## model_name2401 -15587.33 3196.32 -4.877 1.08e-06 ***
## model_name2402 -13932.32 4288.31 -3.249 0.001160 **
## model_name2410 -15210.41 1750.69 -8.688 < 2e-16 ***
## model_name244 -15382.33 4288.31 -3.587 0.000335 ***
## model_name25 -15044.58 1717.97 -8.757 < 2e-16 ***
## model_name264 -15632.33 4288.31 -3.645 0.000267 ***
## model_name2705 -14136.93 1770.04 -7.987 1.42e-15 ***
## model_name2752 -14932.33 3196.32 -4.672 3.00e-06 ***
## model_name2757 -13632.33 4288.31 -3.179 0.001479 **
## model_name3 -9762.34 1466.57 -6.657 2.84e-11 ***
## model_name300 -8819.20 1556.74 -5.665 1.48e-08 ***
## model_name3000GT -9732.49 2183.50 -4.457 8.32e-06 ***
## model_name3008 -6960.59 1499.21 -4.643 3.45e-06 ***
## model_name301 -8740.90 2092.48 -4.177 2.96e-05 ***
## model_name306 -14979.68 1503.74 -9.962 < 2e-16 ***
## model_name307 -12372.34 1460.18 -8.473 < 2e-16 ***
## model_name308 -10472.17 1461.38 -7.166 7.87e-13 ***
## model_name309 -15498.99 2737.16 -5.662 1.50e-08 ***
## model_name3102 -15076.64 3196.32 -4.717 2.40e-06 ***
## model_name310210 -15682.33 4288.31 -3.657 0.000256 ***
## model_name310221 -15232.33 4288.31 -3.552 0.000383 ***
## model_name31029 -15623.61 2092.48 -7.467 8.41e-14 ***
## model_name3110 -15643.17 1669.22 -9.372 < 2e-16 ***
## model_name31105 -15179.98 2183.50 -6.952 3.66e-12 ***
## model_name315 -9095.83 2021.53 -4.499 6.83e-06 ***
## model_name3151 -14657.06 2475.86 -5.920 3.25e-09 ***
## model_name31512 -14257.58 2475.86 -5.759 8.55e-09 ***
## model_name31514 -14482.33 2737.16 -5.291 1.22e-07 ***
## model_name316 -13803.12 1482.89 -9.308 < 2e-16 ***
## model_name318 -12031.99 1459.38 -8.245 < 2e-16 ***
## model_name320 -8265.91 1453.67 -5.686 1.31e-08 ***
## model_name322 -11782.33 3196.32 -3.686 0.000228 ***
## model_name3221 -14712.03 1917.79 -7.671 1.74e-14 ***
## model_name322132 -15382.33 4288.31 -3.587 0.000335 ***
## model_name322133 -15268.74 2737.16 -5.578 2.45e-08 ***
## model_name323 -15019.29 1453.77 -10.331 < 2e-16 ***
## model_name3234 -15683.83 4288.31 -3.657 0.000255 ***
## model_name324 -15757.08 1845.39 -8.539 < 2e-16 ***
## model_name325 -10697.77 1535.46 -6.967 3.29e-12 ***
## model_name328 -8052.11 1704.00 -4.725 2.30e-06 ***
## model_name33 -15632.33 4288.31 -3.645 0.000267 ***
## model_name330 -7676.01 1791.89 -4.284 1.84e-05 ***
## model_name3302 -13010.64 1750.69 -7.432 1.09e-13 ***
## model_name3303 -14946.43 2304.90 -6.485 9.01e-11 ***
## model_name335 -3763.08 2021.53 -1.861 0.062681 .
## model_name340 -14694.96 2304.90 -6.376 1.84e-10 ***
## model_name3500 -6732.33 4288.31 -1.570 0.116441
## model_name350Z -5632.33 3196.32 -1.762 0.078056 .
## model_name360 -16032.33 4288.31 -3.739 0.000185 ***
## model_name3741 -16252.92 4288.31 -3.790 0.000151 ***
## model_name39099 -14185.56 1878.65 -7.551 4.42e-14 ***
## model_name3962 -14373.53 3196.32 -4.497 6.92e-06 ***
## model_name400 -16082.33 4288.31 -3.750 0.000177 ***
## model_name400-Series -15199.42 1510.03 -10.066 < 2e-16 ***
## model_name4007 -6518.69 1878.65 -3.470 0.000521 ***
## model_name4008 -633.33 4288.31 -0.148 0.882591
## model_name401 -13482.33 3196.32 -4.218 2.47e-05 ***
## model_name402 -15957.33 3196.32 -4.992 5.99e-07 ***
## model_name403 -16442.62 4288.31 -3.834 0.000126 ***
## model_name405 -15807.06 1507.82 -10.483 < 2e-16 ***
## model_name406 -14000.99 1443.15 -9.702 < 2e-16 ***
## model_name407 -11480.87 1479.61 -7.759 8.75e-15 ***
## model_name408 -9254.16 1593.31 -5.808 6.37e-09 ***
## model_name410 -15257.00 4288.31 -3.558 0.000374 ***
## model_name412 -15349.45 1791.89 -8.566 < 2e-16 ***
## model_name420 6901.01 2737.16 2.521 0.011699 *
## model_name423 Kombi -16483.32 4288.31 -3.844 0.000121 ***
## model_name428 8211.30 2021.53 4.062 4.88e-05 ***
## model_name435 16455.17 2475.86 6.646 3.05e-11 ***
## model_name440 -15940.38 1791.89 -8.896 < 2e-16 ***
## model_name45 -14434.54 1679.79 -8.593 < 2e-16 ***
## model_name451 -15632.32 3196.32 -4.891 1.01e-06 ***
## model_name452 -15144.83 2475.86 -6.117 9.63e-10 ***
## model_name460 -15727.89 1603.31 -9.810 < 2e-16 ***
## model_name469 -14755.85 1733.45 -8.512 < 2e-16 ***
## model_name4Runner -5672.53 2304.90 -2.461 0.013857 *
## model_name4x4 -12697.03 1791.89 -7.086 1.41e-12 ***
## model_name5 -9332.59 1634.62 -5.709 1.14e-08 ***
## model_name500 -6068.33 2092.48 -2.900 0.003733 **
## model_name5008 -4796.54 1598.16 -3.001 0.002690 **
## model_name500L -5594.83 2475.86 -2.260 0.023842 *
## model_name500X -2732.33 4288.31 -0.637 0.524027
## model_name508 -4074.20 1770.04 -2.302 0.021354 *
## model_name518 -14022.30 1650.57 -8.495 < 2e-16 ***
## model_name520 -10162.77 1449.15 -7.013 2.37e-12 ***
## model_name523 -11810.40 1513.59 -7.803 6.21e-15 ***
## model_name524 -15019.83 1845.39 -8.139 4.10e-16 ***
## model_name525 -10955.78 1445.54 -7.579 3.56e-14 ***
## model_name528 -7658.46 1548.76 -4.945 7.65e-07 ***
## model_name530 -6233.22 1470.00 -4.240 2.24e-05 ***
## model_name535 -2994.28 1679.79 -1.783 0.074670 .
## model_name539 -9732.66 2737.16 -3.556 0.000377 ***
## model_name540 -11932.58 2475.86 -4.820 1.44e-06 ***
## model_name545 -6282.33 3196.32 -1.965 0.049365 *
## model_name550 667.67 2737.16 0.244 0.807287
## model_name6 -8545.10 1450.54 -5.891 3.87e-09 ***
## model_name600-Series -15284.04 1533.53 -9.967 < 2e-16 ***
## model_name605 -15201.13 1484.43 -10.240 < 2e-16 ***
## model_name607 -12055.76 1496.74 -8.055 8.20e-16 ***
## model_name620 -14632.33 4288.31 -3.412 0.000645 ***
## model_name626 -15255.81 1447.14 -10.542 < 2e-16 ***
## model_name630 -2642.53 2304.90 -1.146 0.251603
## model_name635 834.34 2737.16 0.305 0.760505
## model_name640 16007.67 2304.90 6.945 3.84e-12 ***
## model_name645 -5819.95 2021.53 -2.879 0.003992 **
## model_name650 3398.99 2021.53 1.681 0.092694 .
## model_name69 -13516.52 1717.97 -7.868 3.71e-15 ***
## model_name725 -12585.05 1878.65 -6.699 2.13e-11 ***
## model_name728 -12373.99 1717.97 -7.203 6.02e-13 ***
## model_name730 -6072.94 1481.43 -4.099 4.15e-05 ***
## model_name732 -15132.33 4288.31 -3.529 0.000418 ***
## model_name735 -11776.94 1559.64 -7.551 4.42e-14 ***
## model_name740 -7259.04 1503.74 -4.827 1.39e-06 ***
## model_name745 -9381.33 1580.30 -5.936 2.94e-09 ***
## model_name75 -13236.49 1634.62 -8.098 5.77e-16 ***
## model_name750 -4071.78 1584.40 -2.570 0.010176 *
## model_name760 -6182.33 1964.57 -3.147 0.001651 **
## model_name80 -15012.04 1448.12 -10.367 < 2e-16 ***
## model_name800-Series -15444.83 2475.86 -6.238 4.47e-10 ***
## model_name806 -13273.55 1526.46 -8.696 < 2e-16 ***
## model_name807 -10618.64 1588.73 -6.684 2.36e-11 ***
## model_name850 -14355.85 1733.45 -8.282 < 2e-16 ***
## model_name9 -15187.33 4288.31 -3.542 0.000398 ***
## model_name9-2X -11042.33 4288.31 -2.575 0.010028 *
## model_name9 - 3 -11491.91 1548.76 -7.420 1.20e-13 ***
## model_name9 - 5 -12435.81 1580.30 -7.869 3.66e-15 ***
## model_name9 - 7X -7132.33 3196.32 -2.231 0.025659 *
## model_name90 -13751.08 2021.53 -6.802 1.04e-11 ***
## model_name900 -14932.33 2475.86 -6.031 1.64e-09 ***
## model_name9000 -15201.83 1704.00 -8.921 < 2e-16 ***
## model_name911 -3432.33 4288.31 -0.800 0.423489
## model_name929 -15382.74 2183.50 -7.045 1.89e-12 ***
## model_name940 -15061.42 2092.48 -7.198 6.23e-13 ***
## model_name960 -14342.33 2304.90 -6.223 4.94e-10 ***
## model_name965 -13798.99 2737.16 -5.041 4.64e-07 ***
## model_name966 -16032.33 4288.31 -3.739 0.000185 ***
## model_name968M -16214.46 1791.89 -9.049 < 2e-16 ***
## model_nameA1 -7091.15 1964.57 -3.610 0.000307 ***
## model_nameA13 -12632.33 1917.79 -6.587 4.55e-11 ***
## model_nameA140 -13562.96 1750.69 -7.747 9.63e-15 ***
## model_nameA150 -10689.89 1816.78 -5.884 4.04e-09 ***
## model_nameA160 -13354.47 1791.89 -7.453 9.34e-14 ***
## model_nameA170 -12701.42 1679.79 -7.561 4.08e-14 ***
## model_nameA180 -8749.10 1964.57 -4.453 8.48e-06 ***
## model_nameA190 -15082.33 2737.16 -5.510 3.61e-08 ***
## model_nameA2 -11132.33 4288.31 -2.596 0.009436 **
## model_nameA200 7693.13 2304.90 3.338 0.000845 ***
## model_nameA210 -12932.33 2737.16 -4.725 2.31e-06 ***
## model_nameA3 -8872.04 1490.29 -5.953 2.65e-09 ***
## model_nameA4 -9050.76 1440.71 -6.282 3.38e-10 ***
## model_nameA4 Allroad -3032.53 2304.90 -1.316 0.188287
## model_nameA5 -1278.57 1541.72 -0.829 0.406931
## model_nameA6 -8837.12 1437.74 -6.147 8.00e-10 ***
## model_nameA6 Allroad -7460.50 1543.97 -4.832 1.36e-06 ***
## model_nameA7 3670.92 1845.39 1.989 0.046682 *
## model_nameA8 -6927.42 1465.86 -4.726 2.30e-06 ***
## model_nameAccent -11525.34 1474.12 -7.818 5.49e-15 ***
## model_nameAccord -11162.37 1453.36 -7.680 1.62e-14 ***
## model_nameActyon -7211.33 1691.33 -4.264 2.02e-05 ***
## model_nameAerio -12364.11 4288.31 -2.883 0.003939 **
## model_nameAerodeck -14633.72 2475.86 -5.911 3.44e-09 ***
## model_nameAerostar -12732.33 4288.31 -2.969 0.002989 **
## model_nameAgila -13131.09 1704.00 -7.706 1.33e-14 ***
## model_nameAlbea -11847.33 3196.32 -3.707 0.000210 ***
## model_nameAlero -15832.33 4288.31 -3.692 0.000223 ***
## model_nameAlhambra -10113.53 1514.85 -6.676 2.49e-11 ***
## model_nameAlmera -13539.58 1459.07 -9.280 < 2e-16 ***
## model_nameAlmera Tino -13351.91 1565.87 -8.527 < 2e-16 ***
## model_nameAlphard 8067.67 4288.31 1.881 0.059936 .
## model_nameAltea -9191.85 1669.22 -5.507 3.68e-08 ***
## model_nameAltima -9989.69 1816.78 -5.499 3.85e-08 ***
## model_nameAlto -14232.23 1878.65 -7.576 3.65e-14 ***
## model_nameAmarok 4792.42 2475.86 1.936 0.052916 .
## model_nameAMG GT4 -15132.33 4288.31 -3.529 0.000418 ***
## model_nameAmulet -14644.33 2304.90 -6.354 2.13e-10 ***
## model_nameAntara -5826.82 1614.61 -3.609 0.000308 ***
## model_nameArmada -5582.33 3196.32 -1.746 0.080735 .
## model_nameArosa -14944.83 2475.86 -6.036 1.59e-09 ***
## model_nameAscona -15973.42 1704.00 -9.374 < 2e-16 ***
## model_nameAspen -4132.33 4288.31 -0.964 0.335240
## model_nameAstra -12114.05 1437.03 -8.430 < 2e-16 ***
## model_nameAstro Van -9382.33 4288.31 -2.188 0.028684 *
## model_nameASX -4908.43 1572.72 -3.121 0.001804 **
## model_nameAtlas 2371.19 1964.57 1.207 0.227449
## model_nameAtos -14823.99 1845.39 -8.033 9.79e-16 ***
## model_nameAuris -8248.30 1548.76 -5.326 1.01e-07 ***
## model_nameAvella -16145.20 3196.32 -5.051 4.41e-07 ***
## model_nameAvenger -9744.53 2304.90 -4.228 2.37e-05 ***
## model_nameAvensis -9018.85 1464.96 -6.156 7.52e-10 ***
## model_nameAvensis Verso -10594.47 1691.33 -6.264 3.79e-10 ***
## model_nameAveo -11686.21 1556.74 -7.507 6.19e-14 ***
## model_nameAviator -10232.33 4288.31 -2.386 0.017033 *
## model_nameAX -15732.83 3196.32 -4.922 8.60e-07 ***
## model_nameAygo -11082.33 1917.79 -5.779 7.59e-09 ***
## model_nameAztek -10282.33 3196.32 -3.217 0.001297 **
## model_nameB-Max -5532.58 2475.86 -2.235 0.025449 *
## model_nameB150 -10282.33 4288.31 -2.398 0.016501 *
## model_nameB160 -5232.49 2183.50 -2.396 0.016563 *
## model_nameB170 -11482.33 3196.32 -3.592 0.000328 ***
## model_nameB180 -5436.15 1691.33 -3.214 0.001310 **
## model_nameB200 -8933.82 1770.04 -5.047 4.50e-07 ***
## model_nameBaleno -15307.55 1569.21 -9.755 < 2e-16 ***
## model_nameBedford Blitz -15832.33 4288.31 -3.692 0.000223 ***
## model_nameBeetle -8848.99 2183.50 -4.053 5.07e-05 ***
## model_nameBerlingo -11878.98 1516.15 -7.835 4.81e-15 ***
## model_nameBesta -14482.32 3196.32 -4.531 5.89e-06 ***
## model_nameBipper -11169.83 2475.86 -4.511 6.46e-06 ***
## model_nameBipper Tepee -10832.33 4288.31 -2.526 0.011541 *
## model_nameBlazer -12973.61 2092.48 -6.200 5.70e-10 ***
## model_nameBluebird -16157.50 2092.48 -7.722 1.18e-14 ***
## model_nameBonneville -15232.33 4288.31 -3.552 0.000383 ***
## model_nameBora -12974.29 1598.16 -8.118 4.87e-16 ***
## model_nameBoxer -9467.10 1523.27 -6.215 5.19e-10 ***
## model_nameBoxster -7632.33 4288.31 -1.780 0.075117 .
## model_nameBrava -15441.88 1499.21 -10.300 < 2e-16 ***
## model_nameBravo -14809.36 1512.37 -9.792 < 2e-16 ***
## model_nameBreez -15112.33 3196.32 -4.728 2.28e-06 ***
## model_nameBrera 5367.67 4288.31 1.252 0.210687
## model_nameBT-50 -8132.33 4288.31 -1.896 0.057916 .
## model_nameBX -15813.77 3196.32 -4.947 7.55e-07 ***
## model_nameC-Crosser -6042.33 2304.90 -2.622 0.008758 **
## model_nameC-ELYSÉE -8611.85 1733.45 -4.968 6.79e-07 ***
## model_nameC-Max -10163.21 1512.37 -6.720 1.84e-11 ***
## model_nameC1 -12140.66 2183.50 -5.560 2.71e-08 ***
## model_nameC1500 -8832.33 4288.31 -2.060 0.039441 *
## model_nameC180 -8796.05 1479.17 -5.947 2.76e-09 ***
## model_nameC2 -13004.66 1964.57 -6.620 3.65e-11 ***
## model_nameC200 -11132.51 1502.79 -7.408 1.31e-13 ***
## model_nameC220 -10574.42 1501.86 -7.041 1.94e-12 ***
## model_nameC230 -10368.00 1704.00 -6.085 1.18e-09 ***
## model_nameC240 -11613.58 2021.53 -5.745 9.27e-09 ***
## model_nameC25 -13794.83 2475.86 -5.572 2.54e-08 ***
## model_nameC250 -5432.53 1917.79 -2.833 0.004618 **
## model_nameC270 -9682.33 3196.32 -3.029 0.002454 **
## model_nameC280 -14469.83 2475.86 -5.844 5.13e-09 ***
## model_nameC3 -11613.33 1526.46 -7.608 2.85e-14 ***
## model_nameC3 Picasso -9682.20 1878.65 -5.154 2.57e-07 ***
## model_nameC30 -8359.03 2475.86 -3.376 0.000736 ***
## model_nameC300 -10332.33 3196.32 -3.233 0.001228 **
## model_nameC320 -8265.66 2737.16 -3.020 0.002531 **
## model_nameC350 -7132.33 4288.31 -1.663 0.096280 .
## model_nameC4 -10173.33 1471.80 -6.912 4.85e-12 ***
## model_nameC4 AirCross -6057.33 3196.32 -1.895 0.058087 .
## model_nameC4 Grand Picasso -6718.01 1504.72 -4.465 8.04e-06 ***
## model_nameC4 Picasso -7959.00 1548.76 -5.139 2.78e-07 ***
## model_nameC5 -10956.65 1447.42 -7.570 3.82e-14 ***
## model_nameC6 -8584.83 2475.86 -3.467 0.000526 ***
## model_nameC63AMG 12367.67 4288.31 2.884 0.003928 **
## model_nameC70 -12782.47 2092.48 -6.109 1.01e-09 ***
## model_nameC8 -10436.59 1634.62 -6.385 1.74e-10 ***
## model_nameCabstar -8132.33 4288.31 -1.896 0.057916 .
## model_nameCaddy -8325.61 1511.18 -5.509 3.63e-08 ***
## model_nameCaliber -10001.17 1598.16 -6.258 3.94e-10 ***
## model_nameCalibra -15040.19 1878.65 -8.006 1.22e-15 ***
## model_nameCamaro 4532.77 1917.79 2.364 0.018106 *
## model_nameCampo -15032.33 4288.31 -3.505 0.000456 ***
## model_nameCamry -5842.62 1470.29 -3.974 7.09e-05 ***
## model_nameCaptiva -4862.42 1559.64 -3.118 0.001824 **
## model_nameCaptur -2833.21 1964.57 -1.442 0.149268
## model_nameCaravan -13430.84 1499.21 -8.959 < 2e-16 ***
## model_nameCarens -12161.30 1588.73 -7.655 1.98e-14 ***
## model_nameCarina E -14718.65 1551.30 -9.488 < 2e-16 ***
## model_nameCarina II -16144.83 2475.86 -6.521 7.08e-11 ***
## model_nameCarisma -14933.10 1486.07 -10.049 < 2e-16 ***
## model_nameCarnival -13173.68 1572.72 -8.376 < 2e-16 ***
## model_nameCascada 6267.67 4288.31 1.462 0.143867
## model_nameCatera -14032.33 3196.32 -4.390 1.14e-05 ***
## model_nameCavalier -14582.33 3196.32 -4.562 5.08e-06 ***
## model_nameCayenne 323.81 1535.46 0.211 0.832974
## model_nameCebrium -8968.83 3196.32 -2.806 0.005019 **
## model_nameCee'd -7829.16 1476.32 -5.303 1.14e-07 ***
## model_nameCelica -11945.83 1845.39 -6.473 9.71e-11 ***
## model_nameCerato -8306.15 1642.30 -5.058 4.26e-07 ***
## model_nameChallenger 8867.67 4288.31 2.068 0.038659 *
## model_nameChance -14429.54 2737.16 -5.272 1.36e-07 ***
## model_nameCharger 7267.67 4288.31 1.695 0.090128 .
## model_nameChaser -12332.33 3196.32 -3.858 0.000114 ***
## model_nameCherokee -8722.53 1917.79 -4.548 5.43e-06 ***
## model_nameCinquecento -15965.66 2737.16 -5.833 5.49e-09 ***
## model_nameCirrus -14475.18 2092.48 -6.918 4.66e-12 ***
## model_nameCitan -3274.33 2304.90 -1.421 0.155443
## model_nameCitigo -11132.33 4288.31 -2.596 0.009436 **
## model_nameCity -9882.33 3196.32 -3.092 0.001991 **
## model_nameCivic -11366.52 1449.09 -7.844 4.48e-15 ***
## model_nameCK -15152.59 1917.79 -7.901 2.84e-15 ***
## model_nameCL -14282.33 4288.31 -3.331 0.000868 ***
## model_nameCL420 -9632.33 4288.31 -2.246 0.024698 *
## model_nameCL500 -4294.83 2021.53 -2.125 0.033631 *
## model_nameCL550 -132.33 4288.31 -0.031 0.975384
## model_nameCLA180 2817.17 3196.32 0.881 0.378117
## model_nameCLA200 5797.53 1964.57 2.951 0.003169 **
## model_nameCLA220 5267.67 4288.31 1.228 0.219312
## model_nameCLA250 9267.67 4288.31 2.161 0.030690 *
## model_nameCLA45 AMG 18667.67 2737.16 6.820 9.24e-12 ***
## model_nameClarus -15602.29 1541.72 -10.120 < 2e-16 ***
## model_nameClio -13427.59 1485.51 -9.039 < 2e-16 ***
## model_nameCLK200 -11541.49 1845.39 -6.254 4.04e-10 ***
## model_nameCLK230 -11307.33 2475.86 -4.567 4.96e-06 ***
## model_nameCLK240 -10132.33 4288.31 -2.363 0.018144 *
## model_nameCLK270 -9482.33 3196.32 -2.967 0.003013 **
## model_nameCLK280 -7532.33 4288.31 -1.756 0.079015 .
## model_nameCLK320 -9610.90 2092.48 -4.593 4.38e-06 ***
## model_nameCLS250 15378.67 2737.16 5.618 1.94e-08 ***
## model_nameCLS320 -1382.33 3196.32 -0.432 0.665399
## model_nameCLS350 -3176.93 1669.22 -1.903 0.057017 .
## model_nameCLS400 23134.34 2737.16 8.452 < 2e-16 ***
## model_nameCLS500 -432.33 2737.16 -0.158 0.874500
## model_nameCLS55 AMG 267.67 4288.31 0.062 0.950229
## model_nameCLS550 -2532.33 4288.31 -0.591 0.554847
## model_nameCLS63 AMG 33267.67 4288.31 7.758 8.86e-15 ***
## model_nameClubman -5965.66 2737.16 -2.180 0.029300 *
## model_nameColorado -5082.33 4288.31 -1.185 0.235962
## model_nameColt -14235.51 1505.73 -9.454 < 2e-16 ***
## model_nameCombo -12349.33 1750.69 -7.054 1.77e-12 ***
## model_nameCommander -5700.06 2475.86 -2.302 0.021326 *
## model_nameCommodore -15632.33 4288.31 -3.645 0.000267 ***
## model_nameCompass -7516.16 2183.50 -3.442 0.000578 ***
## model_nameConcerto -16157.33 3196.32 -5.055 4.32e-07 ***
## model_nameConcorde -14069.83 2021.53 -6.960 3.46e-12 ***
## model_nameContinental -13832.33 4288.31 -3.226 0.001258 **
## model_nameContour -15153.90 2092.48 -7.242 4.50e-13 ***
## model_nameCooper -4305.89 1650.57 -2.609 0.009092 **
## model_nameCooper S -5972.99 1770.04 -3.374 0.000740 ***
## model_nameCordoba -14685.05 1553.96 -9.450 < 2e-16 ***
## model_nameCorolla -11478.50 1463.89 -7.841 4.58e-15 ***
## model_nameCorolla Verso -9439.06 1556.74 -6.063 1.35e-09 ***
## model_nameCorrado -12132.33 4288.31 -2.829 0.004669 **
## model_nameCorsa -12810.42 1468.08 -8.726 < 2e-16 ***
## model_nameCougar -13987.33 1917.79 -7.293 3.08e-13 ***
## model_nameCountryman 2362.14 1770.04 1.335 0.182044
## model_nameCoupe -13645.64 1584.40 -8.612 < 2e-16 ***
## model_nameCourier -15776.42 1964.57 -8.030 9.99e-16 ***
## model_nameCR-V -5642.53 1490.29 -3.786 0.000153 ***
## model_nameCR-Z -7407.33 2475.86 -2.992 0.002775 **
## model_nameCrafter 475.42 1556.74 0.305 0.760067
## model_nameCreta -364.02 1691.33 -0.215 0.829593
## model_nameCroma -15852.88 1878.65 -8.438 < 2e-16 ***
## model_nameCross Polo -10332.33 4288.31 -2.409 0.015983 *
## model_nameCrosstour 3434.34 2737.16 1.255 0.209592
## model_nameCrown -16382.33 4288.31 -3.820 0.000134 ***
## model_nameCrown Victoria -12682.33 4288.31 -2.957 0.003104 **
## model_nameCruze -7900.49 1471.49 -5.369 7.96e-08 ***
## model_nameCRX -15132.33 4288.31 -3.529 0.000418 ***
## model_nameCT -950.69 1878.65 -0.506 0.612825
## model_nameCTS -9165.04 2092.48 -4.380 1.19e-05 ***
## model_nameCX -12632.33 4288.31 -2.946 0.003224 **
## model_nameCX-3 1067.67 3196.32 0.334 0.738357
## model_nameCX-5 1911.57 1608.78 1.188 0.234757
## model_nameCX-7 -8300.53 1507.82 -5.505 3.72e-08 ***
## model_nameCX-9 8212.23 2021.53 4.062 4.87e-05 ***
## model_nameDaily -6580.01 1470.00 -4.476 7.62e-06 ***
## model_nameDakota -11132.33 4288.31 -2.596 0.009436 **
## model_nameDart -5952.33 2304.90 -2.582 0.009813 **
## model_nameDe Ville -8857.33 2475.86 -3.577 0.000347 ***
## model_nameDedra -15260.90 2092.48 -7.293 3.09e-13 ***
## model_nameDeer -12832.33 2737.16 -4.688 2.77e-06 ***
## model_nameDefender 4960.53 2475.86 2.004 0.045124 *
## model_nameDelta -12027.48 1917.79 -6.272 3.61e-10 ***
## model_nameDemio -14623.99 2183.50 -6.697 2.15e-11 ***
## model_nameDiscovery -1013.46 1614.61 -0.628 0.530215
## model_nameDiscovery Sport 17067.47 2304.90 7.405 1.34e-13 ***
## model_nameDoblo -8298.45 1546.31 -5.367 8.07e-08 ***
## model_nameDokker -8282.33 1917.79 -4.319 1.57e-05 ***
## model_nameDS4 -6894.33 2304.90 -2.991 0.002781 **
## model_nameDS5 -2882.33 2304.90 -1.251 0.211117
## model_nameDucato -11204.97 1504.72 -7.447 9.79e-14 ***
## model_nameDurango -5848.99 2183.50 -2.679 0.007394 **
## model_nameDuster -6312.40 1490.95 -4.234 2.30e-05 ***
## model_nameE200 -9902.67 1463.68 -6.766 1.35e-11 ***
## model_nameE220 -10196.88 1475.93 -6.909 4.97e-12 ***
## model_nameE2200 -15469.83 2475.86 -6.248 4.19e-10 ***
## model_nameE230 -13477.77 1546.31 -8.716 < 2e-16 ***
## model_nameE240 -13464.02 1634.62 -8.237 < 2e-16 ***
## model_nameE250 -8313.62 1559.64 -5.330 9.85e-08 ***
## model_nameE260 -14839.89 1750.69 -8.477 < 2e-16 ***
## model_nameE270 -10540.41 1642.30 -6.418 1.40e-10 ***
## model_nameE280 -9908.25 1627.48 -6.088 1.15e-09 ***
## model_nameE290 -13040.66 2183.50 -5.972 2.36e-09 ***
## model_nameE300 -9074.92 1627.48 -5.576 2.48e-08 ***
## model_nameE320 -9962.49 1608.78 -6.193 5.98e-10 ***
## model_nameE350 -666.59 1614.61 -0.413 0.679719
## model_nameE400 21117.67 3196.32 6.607 3.98e-11 ***
## model_nameE420 -10399.33 2737.16 -3.799 0.000145 ***
## model_nameE430 -12582.33 3196.32 -3.937 8.28e-05 ***
## model_nameE450 -7132.33 4288.31 -1.663 0.096280 .
## model_nameE50 AMG -11632.33 4288.31 -2.713 0.006679 **
## model_nameE500 -5632.33 3196.32 -1.762 0.078056 .
## model_nameE63 AMG 17717.67 3196.32 5.543 2.99e-08 ***
## model_nameEC7 -11832.33 2304.90 -5.134 2.86e-07 ***
## model_nameEcho -13769.83 2475.86 -5.562 2.69e-08 ***
## model_nameEclipse -12916.46 1733.45 -7.451 9.44e-14 ***
## model_nameEclipse Cross 5776.14 3196.32 1.807 0.070751 .
## model_nameEconoline -11608.38 2304.90 -5.036 4.77e-07 ***
## model_nameEcoSport -3187.54 1917.79 -1.662 0.096503 .
## model_nameEdge 8884.17 2183.50 4.069 4.74e-05 ***
## model_nameElantra -10834.64 1524.83 -7.105 1.22e-12 ***
## model_nameEldorado -7882.33 3196.32 -2.466 0.013665 *
## model_nameElement -8056.49 1917.79 -4.201 2.66e-05 ***
## model_nameELR 7318.32 2475.86 2.956 0.003120 **
## model_nameElysion -5282.33 4288.31 -1.232 0.218033
## model_nameEmgrand -7313.89 1964.57 -3.723 0.000197 ***
## model_nameEmgrand 7 -6681.40 2183.50 -3.060 0.002215 **
## model_nameEnclave 3867.67 4288.31 0.902 0.367110
## model_nameEncore -4208.90 1565.87 -2.688 0.007193 **
## model_nameEndeavor -9333.33 4288.31 -2.176 0.029527 *
## model_nameEos -8365.66 2737.16 -3.056 0.002242 **
## model_nameEpica -10260.33 1964.57 -5.223 1.77e-07 ***
## model_nameEquinox -5519.83 2021.53 -2.731 0.006326 **
## model_nameEquus -2182.66 2737.16 -0.797 0.425214
## model_nameES -1102.05 1717.97 -0.641 0.521213
## model_nameEscalade -2483.80 1816.78 -1.367 0.171588
## model_nameEscape -5376.83 1496.74 -3.592 0.000328 ***
## model_nameEscort -15851.80 1458.76 -10.867 < 2e-16 ***
## model_nameEspace -10946.89 1483.40 -7.380 1.62e-13 ***
## model_nameEspero -16041.55 1704.00 -9.414 < 2e-16 ***
## model_nameEvasion -13193.08 1514.85 -8.709 < 2e-16 ***
## model_nameEX -3952.33 1917.79 -2.061 0.039322 *
## model_nameEX7 -5970.96 1964.57 -3.039 0.002373 **
## model_nameExcursion 18367.67 4288.31 4.283 1.85e-05 ***
## model_nameExpedition -10732.33 2737.16 -3.921 8.84e-05 ***
## model_nameExpert -10325.18 1704.00 -6.059 1.38e-09 ***
## model_nameExpert Tepee -7159.13 2737.16 -2.616 0.008913 **
## model_nameExplorer -6017.58 1603.31 -3.753 0.000175 ***
## model_nameExpress 5884.34 2183.50 2.695 0.007044 **
## model_nameF-Pace 22567.67 2475.86 9.115 < 2e-16 ***
## model_nameF-Type 33367.67 4288.31 7.781 7.38e-15 ***
## model_nameF150 4201.01 2737.16 1.535 0.124840
## model_nameF250 -399.33 2737.16 -0.146 0.884009
## model_nameFabia -11576.82 1475.93 -7.844 4.49e-15 ***
## model_nameFavorit -16207.33 3196.32 -5.071 3.98e-07 ***
## model_nameFelicia -15701.29 1562.68 -10.048 < 2e-16 ***
## model_nameFiesta -13677.36 1472.76 -9.287 < 2e-16 ***
## model_nameFiorino -13255.81 2183.50 -6.071 1.28e-09 ***
## model_nameFirebird -10632.33 2737.16 -3.884 0.000103 ***
## model_nameFit -7599.69 1669.22 -4.553 5.31e-06 ***
## model_nameFJ Cruiser -632.33 4288.31 -0.147 0.882775
## model_nameFlorid -12632.33 4288.31 -2.946 0.003224 **
## model_nameFluence -8249.24 1620.83 -5.090 3.61e-07 ***
## model_nameFocus -10888.05 1444.40 -7.538 4.88e-14 ***
## model_nameFora -14189.84 2304.90 -6.156 7.52e-10 ***
## model_nameForenza -12607.33 3196.32 -3.944 8.02e-05 ***
## model_nameForester -8377.30 1499.21 -5.588 2.32e-08 ***
## model_nameForza -13022.46 2475.86 -5.260 1.45e-07 ***
## model_nameFox -12626.88 1964.57 -6.427 1.31e-10 ***
## model_nameFR-V -9449.16 2183.50 -4.328 1.51e-05 ***
## model_nameFreelander -9705.47 1546.31 -6.277 3.50e-10 ***
## model_nameFreemont 117.67 4288.31 0.027 0.978108
## model_nameFreestyle -12932.33 3196.32 -4.046 5.22e-05 ***
## model_nameFrontera -12700.88 1548.76 -8.201 2.47e-16 ***
## model_nameFrontier 367.67 4288.31 0.086 0.931674
## model_nameFusion -6990.69 1469.15 -4.758 1.96e-06 ***
## model_nameFX -4068.14 1499.21 -2.714 0.006660 **
## model_nameG -6540.33 1704.00 -3.838 0.000124 ***
## model_nameG270 8867.67 4288.31 2.068 0.038659 *
## model_nameG300 -6165.66 2737.16 -2.253 0.024292 *
## model_nameG320 8767.67 3196.32 2.743 0.006090 **
## model_nameG350 -7432.33 4288.31 -1.733 0.083076 .
## model_nameG400 5288.65 2737.16 1.932 0.053347 .
## model_nameG500 3867.67 3196.32 1.210 0.226271
## model_nameG55 AMG 14617.67 3196.32 4.573 4.82e-06 ***
## model_nameG6 -12932.33 3196.32 -4.046 5.22e-05 ***
## model_nameGalant -14553.01 1474.83 -9.868 < 2e-16 ***
## model_nameGalaxy -11004.80 1460.35 -7.536 4.96e-14 ***
## model_nameGalloper -13348.99 2183.50 -6.114 9.84e-10 ***
## model_nameGenesis -5157.83 3196.32 -1.614 0.106606
## model_nameGentra -9932.33 4288.31 -2.316 0.020556 *
## model_nameGetz -13045.28 1679.79 -7.766 8.30e-15 ***
## model_nameGiulietta -7632.33 3196.32 -2.388 0.016952 *
## model_nameGL320 1318.56 1964.57 0.671 0.502118
## model_nameGL350 9923.03 1669.22 5.945 2.79e-09 ***
## model_nameGL400 21992.67 3196.32 6.881 6.05e-12 ***
## model_nameGL420 -1382.33 4288.31 -0.322 0.747191
## model_nameGL450 -3505.40 1816.78 -1.929 0.053682 .
## model_nameGL500 11867.56 1964.57 6.041 1.55e-09 ***
## model_nameGL550 -3948.99 2737.16 -1.443 0.149104
## model_nameGL63 31867.17 3196.32 9.970 < 2e-16 ***
## model_nameGLA200 7951.55 2183.50 3.642 0.000271 ***
## model_nameGLA250 11977.89 2737.16 4.376 1.21e-05 ***
## model_nameGLA45 AMG 23867.67 4288.31 5.566 2.63e-08 ***
## model_nameGLC200 32784.07 4288.31 7.645 2.14e-14 ***
## model_nameGLC250 20673.99 2475.86 8.350 < 2e-16 ***
## model_nameGLC300 19267.67 4288.31 4.493 7.04e-06 ***
## model_nameGLE300 25742.67 3196.32 8.054 8.26e-16 ***
## model_nameGLE350 26774.40 3196.32 8.377 < 2e-16 ***
## model_nameGLK220 2860.16 2304.90 1.241 0.214648
## model_nameGLK250 4617.67 4288.31 1.077 0.281574
## model_nameGLK300 1634.34 2737.16 0.597 0.550449
## model_nameGLK350 -1537.59 2021.53 -0.761 0.446898
## model_nameGol -14432.33 4288.31 -3.366 0.000765 ***
## model_nameGolf -13027.20 1437.50 -9.062 < 2e-16 ***
## model_nameGolf Plus -9911.64 1598.16 -6.202 5.64e-10 ***
## model_nameGran Turismo 2989.51 1816.78 1.645 0.099876 .
## model_nameGranada -15857.68 2304.90 -6.880 6.08e-12 ***
## model_nameGrand Am -15332.33 4288.31 -3.575 0.000350 ***
## model_nameGrand C-Max -4282.33 2737.16 -1.565 0.117706
## model_nameGrand Caravan -8864.98 1546.31 -5.733 9.94e-09 ***
## model_nameGrand Cherokee -6609.20 1508.91 -4.380 1.19e-05 ***
## model_nameGrand Espace -9417.48 1627.48 -5.787 7.24e-09 ***
## model_nameGrand Modus -10732.33 4288.31 -2.503 0.012329 *
## model_nameGrand Scenic -7735.80 1484.43 -5.211 1.89e-07 ***
## model_nameGrand Starex -4127.08 1964.57 -2.101 0.035669 *
## model_nameGrand Vitara -8306.53 1511.18 -5.497 3.89e-08 ***
## model_nameGrand Voyager -11191.25 1559.64 -7.176 7.34e-13 ***
## model_nameGrande Punto -11875.36 1620.83 -7.327 2.41e-13 ***
## model_nameGrandeur -6795.33 2475.86 -2.745 0.006061 **
## model_nameGrandis -8490.09 1816.78 -4.673 2.98e-06 ***
## model_nameGrandland X 2362.67 4288.31 0.551 0.581666
## model_nameGranta -10996.83 1750.69 -6.281 3.39e-10 ***
## model_nameGS -3205.12 1614.61 -1.985 0.047143 *
## model_nameGT -9652.33 3196.32 -3.020 0.002531 **
## model_nameGTV -12761.92 2475.86 -5.155 2.56e-07 ***
## model_nameGX 4692.51 2183.50 2.149 0.031635 *
## model_nameH-1 -9926.26 1750.69 -5.670 1.44e-08 ***
## model_nameH 100 -15007.33 2475.86 -6.061 1.36e-09 ***
## model_nameH3 -7523.23 1878.65 -4.005 6.22e-05 ***
## model_nameH5 -9132.33 3196.32 -2.857 0.004277 **
## model_nameH6 -7332.33 4288.31 -1.710 0.087304 .
## model_nameHd -2632.33 2737.16 -0.962 0.336207
## model_nameHHR -9557.58 2475.86 -3.860 0.000113 ***
## model_nameHiAce -12506.66 2737.16 -4.569 4.91e-06 ***
## model_nameHighlander 3229.76 1620.83 1.993 0.046306 *
## model_nameHilux 3410.53 2092.48 1.630 0.103131
## model_nameHover -10182.55 1964.57 -5.183 2.19e-07 ***
## model_nameHR-V -11298.05 1845.39 -6.122 9.32e-10 ***
## model_nameHunter -11512.33 2304.90 -4.995 5.92e-07 ***
## model_nameI -11132.33 3196.32 -3.483 0.000497 ***
## model_namei10 -12428.88 2021.53 -6.148 7.91e-10 ***
## model_namei20 -11076.66 1791.89 -6.182 6.41e-10 ***
## model_namei3 15742.17 3196.32 4.925 8.47e-07 ***
## model_namei30 -8956.86 1556.74 -5.754 8.80e-09 ***
## model_namei40 -3451.52 1634.62 -2.112 0.034735 *
## model_nameIbiza -13075.29 1526.46 -8.566 < 2e-16 ***
## model_nameIdea -12552.33 2304.90 -5.446 5.19e-08 ***
## model_nameIgnis -12902.33 2304.90 -5.598 2.19e-08 ***
## model_nameILX 78.34 2737.16 0.029 0.977167
## model_nameImpala -14732.33 4288.31 -3.435 0.000592 ***
## model_nameImpreza -9768.90 1559.64 -6.264 3.80e-10 ***
## model_nameInsight -8474.59 1704.00 -4.973 6.61e-07 ***
## model_nameInsignia -5720.91 1475.93 -3.876 0.000106 ***
## model_nameIntegra -11632.33 3196.32 -3.639 0.000274 ***
## model_nameInterstar -9615.66 2737.16 -3.513 0.000444 ***
## model_nameIntrepid -14078.02 1659.52 -8.483 < 2e-16 ***
## model_nameIQ -9965.66 2737.16 -3.641 0.000272 ***
## model_nameIS -3533.77 1620.83 -2.180 0.029247 *
## model_nameix20 -9432.33 4288.31 -2.200 0.027845 *
## model_nameix35 -3291.27 1572.72 -2.093 0.036381 *
## model_nameix55 -1882.83 3196.32 -0.589 0.555824
## model_nameJ5 -15075.81 1878.65 -8.025 1.05e-15 ***
## model_nameJazz -11757.50 1620.83 -7.254 4.12e-13 ***
## model_nameJetta -11697.18 1453.36 -8.048 8.64e-16 ***
## model_nameJimny -11382.83 3196.32 -3.561 0.000370 ***
## model_nameJohn Cooper Works 6467.67 4288.31 1.508 0.131509
## model_nameJoice -13715.83 2183.50 -6.282 3.39e-10 ***
## model_nameJourney -5076.77 1964.57 -2.584 0.009765 **
## model_nameJuke -5475.40 1517.48 -3.608 0.000309 ***
## model_nameJumper -10576.07 1537.47 -6.879 6.13e-12 ***
## model_nameJumpy -9211.61 1750.69 -5.262 1.44e-07 ***
## model_nameJusty -12113.16 2183.50 -5.548 2.92e-08 ***
## model_nameJX 6417.67 2475.86 2.592 0.009543 **
## model_nameKa -14556.44 1717.97 -8.473 < 2e-16 ***
## model_nameKadett -16190.68 1598.16 -10.131 < 2e-16 ***
## model_nameKadjar -1307.33 2021.53 -0.647 0.517829
## model_nameKalina -11853.54 1964.57 -6.034 1.62e-09 ***
## model_nameKalos -13644.83 2475.86 -5.511 3.59e-08 ***
## model_nameKangoo -12874.53 1556.74 -8.270 < 2e-16 ***
## model_nameKapitan -8632.33 4288.31 -2.013 0.044123 *
## model_nameKappa -14811.36 1642.30 -9.019 < 2e-16 ***
## model_nameKaptur -2036.15 1679.79 -1.212 0.225464
## model_nameKimo -14099.33 2737.16 -5.151 2.60e-07 ***
## model_nameKodiaq 18629.13 1474.47 12.634 < 2e-16 ***
## model_nameKoleos -4692.79 1791.89 -2.619 0.008825 **
## model_nameKorando -11082.33 3196.32 -3.467 0.000526 ***
## model_nameKuga -2453.50 1614.61 -1.520 0.128629
## model_nameKyron -8561.70 1750.69 -4.890 1.01e-06 ***
## model_nameL200 -3026.86 1770.04 -1.710 0.087265 .
## model_nameL300 -15232.33 3196.32 -4.766 1.89e-06 ***
## model_nameL400 -13798.99 2737.16 -5.041 4.64e-07 ***
## model_nameL50 -9632.33 4288.31 -2.246 0.024698 *
## model_nameLacetti -12334.62 1627.48 -7.579 3.56e-14 ***
## model_nameLaCrosse 4367.67 4288.31 1.019 0.308443
## model_nameLaguna -13677.64 1439.83 -9.499 < 2e-16 ***
## model_nameLancer -12316.10 1468.61 -8.386 < 2e-16 ***
## model_nameLancer Evolution -3182.33 2304.90 -1.381 0.167386
## model_nameLancia -16482.33 4288.31 -3.844 0.000121 ***
## model_nameLand Cruiser 5562.68 1488.41 3.737 0.000186 ***
## model_nameLanos -15317.61 1548.76 -9.890 < 2e-16 ***
## model_nameLantra -15614.67 1520.28 -10.271 < 2e-16 ***
## model_nameLargus -9859.34 1669.22 -5.907 3.52e-09 ***
## model_nameLatitude -7057.58 2475.86 -2.851 0.004367 **
## model_nameLC -11006.96 2304.90 -4.775 1.80e-06 ***
## model_nameLe Baron -15132.33 4288.31 -3.529 0.000418 ***
## model_nameLeaf -3422.33 2304.90 -1.485 0.137604
## model_nameLegacy -12203.68 1516.15 -8.049 8.58e-16 ***
## model_nameLeganza -15052.32 1669.22 -9.018 < 2e-16 ***
## model_nameLegend -11520.11 1791.89 -6.429 1.30e-10 ***
## model_nameLeMans -14132.33 4288.31 -3.296 0.000983 ***
## model_nameLeon -9472.53 1691.33 -5.601 2.15e-08 ***
## model_nameLeone -15132.33 4288.31 -3.529 0.000418 ***
## model_nameLHS -13600.83 2021.53 -6.728 1.74e-11 ***
## model_nameLiana -13175.72 1717.97 -7.669 1.77e-14 ***
## model_nameLibero -15165.27 3196.32 -4.745 2.10e-06 ***
## model_nameLiberty -10582.33 2475.86 -4.274 1.92e-05 ***
## model_nameLinea -12014.15 1964.57 -6.115 9.73e-10 ***
## model_nameLodgy -6128.47 2092.48 -2.929 0.003405 **
## model_nameLogan -10873.76 1476.32 -7.365 1.80e-13 ***
## model_nameLogo -14432.33 4288.31 -3.366 0.000765 ***
## model_nameLS -3211.80 1733.45 -1.853 0.063913 .
## model_nameLT -12233.49 1486.07 -8.232 < 2e-16 ***
## model_nameLuidor 1817.67 3196.32 0.569 0.569578
## model_nameLumina -14121.33 2737.16 -5.159 2.49e-07 ***
## model_nameLupo -13865.66 1964.57 -7.058 1.72e-12 ***
## model_nameLX 15767.67 2021.53 7.800 6.36e-15 ***
## model_nameLybra -14565.66 1679.79 -8.671 < 2e-16 ***
## model_nameM -5685.50 1733.45 -3.280 0.001039 **
## model_nameM2 23267.67 4288.31 5.426 5.80e-08 ***
## model_nameM3 4081.40 2475.86 1.648 0.099263 .
## model_nameM4 -9732.33 4288.31 -2.270 0.023244 *
## model_nameM6 -2832.33 4288.31 -0.660 0.508953
## model_nameMacan 24134.34 2737.16 8.817 < 2e-16 ***
## model_nameMagentis -11101.18 1750.69 -6.341 2.31e-10 ***
## model_nameMagnum -9532.33 3196.32 -2.982 0.002863 **
## model_nameMalaga -16350.62 3196.32 -5.115 3.15e-07 ***
## model_nameMalibu -4291.68 1603.31 -2.677 0.007437 **
## model_nameManager -14607.33 3196.32 -4.570 4.89e-06 ***
## model_nameMarea -15401.74 1483.40 -10.383 < 2e-16 ***
## model_nameMark VIII -11332.33 3196.32 -3.545 0.000392 ***
## model_nameMascott -6365.66 2183.50 -2.915 0.003555 **
## model_nameMaster -6867.18 1584.40 -4.334 1.47e-05 ***
## model_nameMatiz -14708.18 1528.13 -9.625 < 2e-16 ***
## model_nameMatrix -12528.50 1580.30 -7.928 2.29e-15 ***
## model_nameMaverick -12785.20 2021.53 -6.325 2.57e-10 ***
## model_nameMaxima -13512.49 1608.78 -8.399 < 2e-16 ***
## model_nameMaxity -15732.33 4288.31 -3.669 0.000244 ***
## model_nameMB100 -15143.28 1679.79 -9.015 < 2e-16 ***
## model_nameMDX -887.18 1679.79 -0.528 0.597397
## model_nameMegane -12915.46 1444.52 -8.941 < 2e-16 ***
## model_nameMegane Scenic -13974.08 1470.00 -9.506 < 2e-16 ***
## model_nameMeriva -11163.77 1495.19 -7.466 8.42e-14 ***
## model_nameMicra -13873.69 1588.73 -8.733 < 2e-16 ***
## model_nameMii -10407.83 3196.32 -3.256 0.001130 **
## model_nameMillenia -13897.82 1620.83 -8.575 < 2e-16 ***
## model_nameMirage -14664.83 2475.86 -5.923 3.19e-09 ***
## model_nameMK -13840.20 2021.53 -6.846 7.69e-12 ***
## model_nameMKC 6737.67 2304.90 2.923 0.003467 **
## model_nameMKX -5132.33 4288.31 -1.197 0.231385
## model_nameMKZ -1257.33 2475.86 -0.508 0.611573
## model_nameML230 -11482.33 2737.16 -4.195 2.74e-05 ***
## model_nameML250 18317.67 4288.31 4.272 1.95e-05 ***
## model_nameML270 -8911.33 1704.00 -5.230 1.71e-07 ***
## model_nameML280 -2849.43 2737.16 -1.041 0.297875
## model_nameML300 8640.47 2092.48 4.129 3.65e-05 ***
## model_nameML320 -5777.16 1541.72 -3.747 0.000179 ***
## model_nameML350 1347.53 1505.73 0.895 0.370825
## model_nameML400 -9035.35 1791.89 -5.042 4.62e-07 ***
## model_nameML430 -9712.98 2737.16 -3.549 0.000388 ***
## model_nameML500 -14.33 2304.90 -0.006 0.995041
## model_nameML55 AMG -10887.33 3196.32 -3.406 0.000659 ***
## model_nameML550 -1632.33 3196.32 -0.511 0.609572
## model_nameML63 AMG 19866.67 4288.31 4.633 3.62e-06 ***
## model_nameModel F -15632.33 4288.31 -3.645 0.000267 ***
## model_nameModus -12382.33 2737.16 -4.524 6.09e-06 ***
## model_nameMohave 6467.67 2737.16 2.363 0.018137 *
## model_nameMokka -3646.83 1650.57 -2.209 0.027151 *
## model_nameMondeo -13413.79 1438.40 -9.326 < 2e-16 ***
## model_nameMonterey -13394.83 2475.86 -5.410 6.33e-08 ***
## model_nameMontero -10342.33 1770.04 -5.843 5.17e-09 ***
## model_nameMonza -15067.31 4288.31 -3.514 0.000443 ***
## model_nameMovano -8514.14 1878.65 -4.532 5.86e-06 ***
## model_nameMPV -12464.01 1535.46 -8.117 4.91e-16 ***
## model_nameMR2 -1632.33 4288.31 -0.381 0.703469
## model_nameMultipla -13945.29 1634.62 -8.531 < 2e-16 ***
## model_nameMurano -5809.43 1565.87 -3.710 0.000208 ***
## model_nameMusa -11635.08 2475.86 -4.699 2.62e-06 ***
## model_nameMusso -13488.55 1964.57 -6.866 6.71e-12 ***
## model_nameMustang 2469.16 1717.97 1.437 0.150654
## model_nameMX-3 -15158.99 2183.50 -6.943 3.91e-12 ***
## model_nameMX-6 -12565.66 2737.16 -4.591 4.43e-06 ***
## model_nameMyWay -5182.33 3196.32 -1.621 0.104953
## model_nameNavara -6091.76 1750.69 -3.480 0.000503 ***
## model_nameNavigator -10112.33 2304.90 -4.387 1.15e-05 ***
## model_nameNemo -12082.47 2092.48 -5.774 7.79e-09 ***
## model_nameNeon -14823.24 1548.76 -9.571 < 2e-16 ***
## model_nameNexia -15195.98 1627.48 -9.337 < 2e-16 ***
## model_nameNitro -6607.33 3196.32 -2.067 0.038725 *
## model_nameNiva -11445.96 1878.65 -6.093 1.12e-09 ***
## model_nameNote -10368.47 1627.48 -6.371 1.90e-10 ***
## model_nameNubira -14987.49 1539.55 -9.735 < 2e-16 ***
## model_nameNV S -3632.33 4288.31 -0.847 0.396984
## model_nameNV200 -8632.33 4288.31 -2.013 0.044123 *
## model_nameNX 19061.90 1964.57 9.703 < 2e-16 ***
## model_nameOctavia -5277.79 1444.97 -3.653 0.000260 ***
## model_nameOdyssey 5022.36 2183.50 2.300 0.021446 *
## model_nameOmega -14645.55 1444.14 -10.141 < 2e-16 ***
## model_nameOne -7937.85 1964.57 -4.040 5.34e-05 ***
## model_nameOptima -3902.68 1691.33 -2.307 0.021035 *
## model_nameOrion -16208.22 1733.45 -9.350 < 2e-16 ***
## model_nameOrlando -5250.18 1791.89 -2.930 0.003392 **
## model_nameOutback -7758.53 1512.37 -5.130 2.91e-07 ***
## model_nameOutlander -5198.43 1483.91 -3.503 0.000460 ***
## model_namePaceman 2467.67 4288.31 0.575 0.564996
## model_namePacifica -9176.26 1551.30 -5.915 3.34e-09 ***
## model_namePajero -6803.58 1508.91 -4.509 6.54e-06 ***
## model_namePajero Pinin -11722.33 2475.86 -4.735 2.20e-06 ***
## model_namePajero Sport -5204.76 1620.83 -3.211 0.001323 **
## model_namePalio -15544.29 1608.78 -9.662 < 2e-16 ***
## model_namePanamera 10917.67 2475.86 4.410 1.04e-05 ***
## model_namePanda -12882.48 1816.78 -7.091 1.36e-12 ***
## model_namePartner -12175.85 1548.76 -7.862 3.89e-15 ***
## model_namePartner Tepee -8944.41 1845.39 -4.847 1.26e-06 ***
## model_namePaseo -15115.66 2737.16 -5.522 3.37e-08 ***
## model_namePassat -11527.59 1433.45 -8.042 9.11e-16 ***
## model_namePassat CC -5431.69 1521.75 -3.569 0.000358 ***
## model_namePassport -14166.24 4288.31 -3.303 0.000956 ***
## model_namePathfinder -4096.11 1593.31 -2.571 0.010150 *
## model_namePatriot -7534.82 1691.33 -4.455 8.42e-06 ***
## model_namePatrol -4408.45 1593.31 -2.767 0.005663 **
## model_namePegasus -11132.33 4288.31 -2.596 0.009436 **
## model_namePeri -14365.99 2737.16 -5.248 1.54e-07 ***
## model_namePhaeton -7719.09 1770.04 -4.361 1.30e-05 ***
## model_namePhedra -10688.75 2092.48 -5.108 3.27e-07 ***
## model_namePicanto -11162.93 1917.79 -5.821 5.91e-09 ***
## model_namePicnic -12755.01 1816.78 -7.021 2.25e-12 ***
## model_namePilot -2132.66 1845.39 -1.156 0.247825
## model_namePixo -12020.24 2737.16 -4.391 1.13e-05 ***
## model_namePointer -13382.33 4288.31 -3.121 0.001806 **
## model_namePolo -12545.14 1469.15 -8.539 < 2e-16 ***
## model_namePolo Sedan -7545.87 1471.49 -5.128 2.94e-07 ***
## model_namePony -15876.51 1878.65 -8.451 < 2e-16 ***
## model_namePorter -10632.33 4288.31 -2.479 0.013166 *
## model_namePrairie -15594.83 2475.86 -6.299 3.03e-10 ***
## model_namePregio -14132.33 4288.31 -3.296 0.000983 ***
## model_namePrelude -13103.66 1845.39 -7.101 1.26e-12 ***
## model_namePremacy -13407.58 1598.16 -8.389 < 2e-16 ***
## model_namePrevia -12453.68 1878.65 -6.629 3.42e-11 ***
## model_namePride -15832.33 2737.16 -5.784 7.34e-09 ***
## model_namePrimastar -8682.33 3196.32 -2.716 0.006604 **
## model_namePrimera -14317.76 1445.87 -9.903 < 2e-16 ***
## model_namePriora -12886.34 1750.69 -7.361 1.87e-13 ***
## model_namePrius -3215.10 1517.48 -2.119 0.034122 *
## model_namePro Cee'd -9232.33 4288.31 -2.153 0.031332 *
## model_nameProbe -14940.05 1917.79 -7.790 6.86e-15 ***
## model_nameProtege -14444.25 2183.50 -6.615 3.76e-11 ***
## model_namePT Cruiser -12640.48 1634.62 -7.733 1.08e-14 ***
## model_namePulsar -5332.33 4288.31 -1.243 0.213708
## model_namePuma -15232.66 2737.16 -5.565 2.64e-08 ***
## model_namePunto -14695.58 1479.17 -9.935 < 2e-16 ***
## model_nameQ 3197.67 2304.90 1.387 0.165346
## model_nameQ3 1196.72 1691.33 0.708 0.479221
## model_nameQ5 1012.85 1511.18 0.670 0.502712
## model_nameQ7 3020.08 1500.07 2.013 0.044091 *
## model_nameQashqai -4338.53 1464.96 -2.962 0.003063 **
## model_nameQashqai+2 -5229.69 1704.00 -3.069 0.002149 **
## model_nameQQ -14640.83 2183.50 -6.705 2.04e-11 ***
## model_nameQQ6 -14651.72 2737.16 -5.353 8.71e-08 ***
## model_nameQuest -8965.66 2737.16 -3.276 0.001056 **
## model_nameQuoris 1400.34 2737.16 0.512 0.608932
## model_nameQX 4271.55 1642.30 2.601 0.009300 **
## model_nameR280 -5632.33 3196.32 -1.762 0.078056 .
## model_nameR320 -2633.33 4288.31 -0.614 0.539172
## model_nameR350 -4711.58 1845.39 -2.553 0.010679 *
## model_nameR500 -7732.33 4288.31 -1.803 0.071378 .
## model_nameRam 5475.51 2183.50 2.508 0.012157 *
## model_nameRange Rover -3831.42 1562.68 -2.452 0.014218 *
## model_nameRange Rover Evoque 7923.07 1691.33 4.685 2.82e-06 ***
## model_nameRange Rover Sport 3015.32 1572.72 1.917 0.055212 .
## model_nameRanger -5145.18 2092.48 -2.459 0.013941 *
## model_nameRapid -1915.93 1456.16 -1.316 0.188268
## model_nameRAV4 -3848.78 1468.61 -2.621 0.008778 **
## model_nameRDX -1869.83 2475.86 -0.755 0.450120
## model_nameRegal -1269.83 2475.86 -0.513 0.608036
## model_nameRekord -16160.27 2092.48 -7.723 1.16e-14 ***
## model_nameRendez Vous -11332.33 4288.31 -2.643 0.008230 **
## model_nameRenegade 20831.01 2737.16 7.610 2.80e-14 ***
## model_nameRetona -12551.99 2737.16 -4.586 4.54e-06 ***
## model_nameRexton -9152.29 1650.57 -5.545 2.96e-08 ***
## model_nameRezzo -13483.44 1964.57 -6.863 6.83e-12 ***
## model_nameRidgeline -182.33 3196.32 -0.057 0.954512
## model_nameRio -9260.06 1464.31 -6.324 2.58e-10 ***
## model_nameRL -8143.29 4288.31 -1.899 0.057579 .
## model_nameRodius -8423.51 2092.48 -4.026 5.69e-05 ***
## model_nameRogue -5757.33 2475.86 -2.325 0.020057 *
## model_nameRoomster -10308.59 1634.62 -6.306 2.89e-10 ***
## model_nameRS6 -5834.83 2475.86 -2.357 0.018444 *
## model_nameRSX -9982.33 3196.32 -3.123 0.001791 **
## model_nameRVR -7132.33 4288.31 -1.663 0.096280 .
## model_nameRX 806.18 1495.19 0.539 0.589764
## model_nameRX-8 -10046.61 2092.48 -4.801 1.58e-06 ***
## model_nameS-Coupe -15732.33 4288.31 -3.669 0.000244 ***
## model_nameS-Max -5628.55 1546.31 -3.640 0.000273 ***
## model_nameS-Type -11612.33 2737.16 -4.242 2.22e-05 ***
## model_nameS1000 -14545.64 4288.31 -3.392 0.000695 ***
## model_nameS2000 13367.67 4288.31 3.117 0.001827 **
## model_nameS220 -9515.66 2183.50 -4.358 1.32e-05 ***
## model_nameS260 -14132.33 4288.31 -3.296 0.000983 ***
## model_nameS280 -15632.33 4288.31 -3.645 0.000267 ***
## model_nameS300 -9969.14 1733.45 -5.751 8.94e-09 ***
## model_nameS320 -9255.71 1539.55 -6.012 1.85e-09 ***
## model_nameS350 -1394.50 1627.48 -0.857 0.391535
## model_nameS4 -8744.83 2021.53 -4.326 1.52e-05 ***
## model_nameS40 -11326.15 1492.30 -7.590 3.28e-14 ***
## model_nameS400 -6801.08 2021.53 -3.364 0.000768 ***
## model_nameS420 -7962.33 3196.32 -2.491 0.012739 *
## model_nameS430 -10922.33 1917.79 -5.695 1.24e-08 ***
## model_nameS450 1967.67 2737.16 0.719 0.472223
## model_nameS5 451.01 2183.50 0.207 0.836360
## model_nameS500 -6066.63 1517.48 -3.998 6.41e-05 ***
## model_nameS550 -2624.43 1917.79 -1.368 0.171175
## model_nameS560 -12632.33 4288.31 -2.946 0.003224 **
## model_nameS6 -10348.99 2183.50 -4.740 2.15e-06 ***
## model_nameS60 -8337.43 1483.91 -5.619 1.94e-08 ***
## model_nameS600 -3965.99 2737.16 -1.449 0.147362
## model_nameS63 AMG 7117.67 3196.32 2.227 0.025964 *
## model_nameS65 AMG 20617.67 3196.32 6.450 1.13e-10 ***
## model_nameS70 -14015.82 1878.65 -7.461 8.80e-14 ***
## model_nameS8 -4215.83 2183.50 -1.931 0.053520 .
## model_nameS80 -8412.87 1475.56 -5.701 1.20e-08 ***
## model_nameS90 -13812.33 2304.90 -5.993 2.08e-09 ***
## model_nameSafe -13105.23 2475.86 -5.293 1.21e-07 ***
## model_nameSafrane -15145.41 1486.64 -10.188 < 2e-16 ***
## model_nameSamurai -11632.33 3196.32 -3.639 0.000274 ***
## model_nameSandero -8488.06 1487.80 -5.705 1.17e-08 ***
## model_nameSanta Fe -4095.11 1454.52 -2.815 0.004874 **
## model_nameSantamo -14591.74 2092.48 -6.973 3.14e-12 ***
## model_nameSantana -16205.68 2304.90 -7.031 2.09e-12 ***
## model_nameSaxo -15078.05 1878.65 -8.026 1.04e-15 ***
## model_nameSC -5732.83 3196.32 -1.794 0.072890 .
## model_nameSC7 -11452.43 1917.79 -5.972 2.37e-09 ***
## model_nameScenic -12526.83 1447.03 -8.657 < 2e-16 ***
## model_nameScirocco -8899.10 1964.57 -4.530 5.92e-06 ***
## model_nameScorpio -15597.97 1516.15 -10.288 < 2e-16 ***
## model_nameScudo -11801.95 1614.61 -7.309 2.74e-13 ***
## model_nameSebring -12965.16 1584.40 -8.183 2.86e-16 ***
## model_nameSedona -11407.33 3196.32 -3.569 0.000359 ***
## model_nameSeicento -15107.33 3196.32 -4.726 2.29e-06 ***
## model_nameSens -14262.53 2304.90 -6.188 6.16e-10 ***
## model_nameSentra -8306.30 1704.00 -4.875 1.09e-06 ***
## model_nameSephia -15907.17 1593.31 -9.984 < 2e-16 ***
## model_nameSequoia 14367.67 4288.31 3.350 0.000808 ***
## model_nameSerena -15118.47 1791.89 -8.437 < 2e-16 ***
## model_nameSeville -14132.33 4288.31 -3.296 0.000983 ***
## model_nameSharan -9382.29 1462.88 -6.414 1.44e-10 ***
## model_nameShuma -15223.60 1642.30 -9.270 < 2e-16 ***
## model_nameShuttle -13648.99 2183.50 -6.251 4.12e-10 ***
## model_nameSiena -15932.33 4288.31 -3.715 0.000203 ***
## model_nameSienna -2486.92 1669.22 -1.490 0.136268
## model_nameSierra -15997.78 1493.00 -10.715 < 2e-16 ***
## model_nameSigma -15082.33 4288.31 -3.517 0.000437 ***
## model_nameSignum -11267.40 1559.64 -7.224 5.13e-13 ***
## model_nameSilver 11267.67 4288.31 2.628 0.008604 **
## model_nameSilverado -1007.33 3196.32 -0.315 0.752648
## model_nameSintra -14275.82 1580.30 -9.034 < 2e-16 ***
## model_nameSkyline -9556.51 2737.16 -3.491 0.000481 ***
## model_nameSL320 -12132.33 4288.31 -2.829 0.004669 **
## model_nameSL350 13358.15 2737.16 4.880 1.06e-06 ***
## model_nameSL380 10882.37 4288.31 2.538 0.011163 *
## model_nameSL500 -1132.99 2737.16 -0.414 0.678928
## model_nameSLC200 31367.67 4288.31 7.315 2.63e-13 ***
## model_nameSLK200 -4732.33 4288.31 -1.104 0.269799
## model_nameSLK350 -3133.33 4288.31 -0.731 0.464987
## model_nameSLX -12432.33 4288.31 -2.899 0.003744 **
## model_nameSmily -13832.33 4288.31 -3.226 0.001258 **
## model_nameSobol -10322.79 3196.32 -3.230 0.001241 **
## model_nameSolano -12607.33 2475.86 -5.092 3.56e-07 ***
## model_nameSolara -11103.76 2092.48 -5.307 1.12e-07 ***
## model_nameSolaris -7719.16 1480.05 -5.215 1.84e-07 ***
## model_nameSonata -11233.21 1477.91 -7.601 3.01e-14 ***
## model_nameSorento -5713.87 1465.86 -3.898 9.72e-05 ***
## model_nameSoul -7172.27 1878.65 -3.818 0.000135 ***
## model_nameSpace Gear -13765.66 2737.16 -5.029 4.95e-07 ***
## model_nameSpace Runner -15508.33 1770.04 -8.762 < 2e-16 ***
## model_nameSpace Star -13833.72 1526.46 -9.063 < 2e-16 ***
## model_nameSpace Wagon -14659.27 1556.74 -9.417 < 2e-16 ***
## model_nameSpark -11292.68 1770.04 -6.380 1.79e-10 ***
## model_nameSpectra -12971.20 2021.53 -6.417 1.41e-10 ***
## model_nameSpider 2367.67 4288.31 0.552 0.580867
## model_nameSplash -10523.53 1917.79 -5.487 4.11e-08 ***
## model_nameSportage -4297.58 1462.11 -2.939 0.003292 **
## model_nameSprinter -5557.94 1454.63 -3.821 0.000133 ***
## model_nameSRX -7498.99 1964.57 -3.817 0.000135 ***
## model_nameStarex -10707.33 2475.86 -4.325 1.53e-05 ***
## model_nameStarlet -15782.33 4288.31 -3.680 0.000233 ***
## model_nameStavic -732.33 4288.31 -0.171 0.864404
## model_nameStealth -10832.33 3196.32 -3.389 0.000702 ***
## model_nameStilo -13381.93 1588.73 -8.423 < 2e-16 ***
## model_nameStratus -14171.23 1508.91 -9.392 < 2e-16 ***
## model_nameStream -11831.19 2021.53 -5.853 4.88e-09 ***
## model_nameStreetwise -12532.33 4288.31 -2.922 0.003475 **
## model_nameSTS -10132.33 4288.31 -2.363 0.018144 *
## model_nameSuburban -9518.61 4288.31 -2.220 0.026448 *
## model_nameSunbird -16282.33 4288.31 -3.797 0.000147 ***
## model_nameSunny -15949.50 1559.64 -10.226 < 2e-16 ***
## model_nameSuperb -3997.81 1471.49 -2.717 0.006594 **
## model_nameSupra -11632.33 4288.31 -2.713 0.006679 **
## model_nameSwift -13491.82 1669.22 -8.083 6.52e-16 ***
## model_nameSX4 -8144.21 1627.48 -5.004 5.64e-07 ***
## model_nameSX4 S-Cross -11132.33 4288.31 -2.596 0.009436 **
## model_nameSymbol -13604.33 1845.39 -7.372 1.72e-13 ***
## model_nameT1 -13825.21 1498.36 -9.227 < 2e-16 ***
## model_nameT2 -11845.49 1603.31 -7.388 1.52e-13 ***
## model_nameT3 -14808.53 1593.31 -9.294 < 2e-16 ***
## model_nameT3 Caravelle -14581.30 2475.86 -5.889 3.91e-09 ***
## model_nameT3 Multivan -14082.33 3196.32 -4.406 1.06e-05 ***
## model_nameT4 -11322.66 1457.33 -7.769 8.08e-15 ***
## model_nameT4 Caravelle -9967.33 1520.28 -6.556 5.59e-11 ***
## model_nameT4 Multivan -8053.91 1562.68 -5.154 2.56e-07 ***
## model_nameT5 -5093.94 1514.85 -3.363 0.000773 ***
## model_nameT5 Caravelle -366.50 1526.46 -0.240 0.810253
## model_nameT5 Multivan 3403.05 1518.86 2.241 0.025062 *
## model_nameT5 Shuttle -4265.66 2737.16 -1.558 0.119141
## model_nameT6 8267.67 2737.16 3.021 0.002525 **
## model_nameT6 Caravelle 7953.07 1964.57 4.048 5.17e-05 ***
## model_nameT6 Multivan 21042.67 3196.32 6.583 4.66e-11 ***
## model_nameTacoma 7080.09 4288.31 1.651 0.098742 .
## model_nameTacuma -13792.33 2304.90 -5.984 2.20e-09 ***
## model_nameTahoe 3187.67 2304.90 1.383 0.166673
## model_nameTalisman -1170.16 2183.50 -0.536 0.592024
## model_nameTaunus -14182.33 2475.86 -5.728 1.02e-08 ***
## model_nameTaurus -13340.66 2183.50 -6.110 1.01e-09 ***
## model_nameTavria -16204.13 1917.79 -8.449 < 2e-16 ***
## model_nameTeana -6872.05 1642.30 -4.184 2.87e-05 ***
## model_nameTempo -16132.33 4288.31 -3.762 0.000169 ***
## model_nameTempra -15967.07 1614.61 -9.889 < 2e-16 ***
## model_nameTeramont 8367.67 4288.31 1.951 0.051032 .
## model_nameTerracan -9708.93 1620.83 -5.990 2.12e-09 ***
## model_nameTerrano -7485.91 1620.83 -4.619 3.88e-06 ***
## model_nameThalia -14132.33 4288.31 -3.296 0.000983 ***
## model_nameThema -16232.33 4288.31 -3.785 0.000154 ***
## model_nameThesis -12057.33 2475.86 -4.870 1.12e-06 ***
## model_nameThunderbird -15232.33 3196.32 -4.766 1.89e-06 ***
## model_nameTiburon -12265.66 1845.39 -6.647 3.04e-11 ***
## model_nameTiggo -9678.61 1642.30 -5.893 3.82e-09 ***
## model_nameTigra -14457.33 1791.89 -8.068 7.35e-16 ***
## model_nameTiguan -2553.14 1484.97 -1.719 0.085564 .
## model_nameTiida -8682.73 1750.69 -4.960 7.10e-07 ***
## model_nameTipo -16006.91 1791.89 -8.933 < 2e-16 ***
## model_nameTitan 1167.67 3196.32 0.365 0.714875
## model_nameTL -8887.99 1964.57 -4.524 6.08e-06 ***
## model_nameTLX 1367.67 4288.31 0.319 0.749781
## model_nameToledo -15101.21 1493.71 -10.110 < 2e-16 ***
## model_nameTouareg -3438.06 1477.50 -2.327 0.019973 *
## model_nameTouran -8021.57 1457.74 -5.503 3.76e-08 ***
## model_nameTourneo -7397.71 1816.78 -4.072 4.67e-05 ***
## model_nameTourneo Custom 6477.67 2475.86 2.616 0.008891 **
## model_nameTown Car -10261.80 1733.45 -5.920 3.25e-09 ***
## model_nameTown&Country -8332.70 1551.30 -5.371 7.86e-08 ***
## model_nameTracker -14232.66 2737.16 -5.200 2.01e-07 ***
## model_nameTrafic -9315.29 1580.30 -5.895 3.79e-09 ***
## model_nameTrail Blazer -7022.40 1878.65 -3.738 0.000186 ***
## model_nameTrajet -12538.55 1717.97 -7.298 2.97e-13 ***
## model_nameTrans Sport -14907.33 2021.53 -7.374 1.69e-13 ***
## model_nameTransit -11665.62 1448.37 -8.054 8.23e-16 ***
## model_nameTrax -6835.16 1717.97 -3.979 6.94e-05 ***
## model_nameTribeca -7619.17 1704.00 -4.471 7.80e-06 ***
## model_nameTribute -10859.48 1816.78 -5.977 2.29e-09 ***
## model_nameTSX -6914.54 1791.89 -3.859 0.000114 ***
## model_nameTT -6953.02 1878.65 -3.701 0.000215 ***
## model_nameTucson -4712.98 1524.83 -3.091 0.001998 **
## model_nameTundra 2117.67 1917.79 1.104 0.269502
## model_nameTwingo -14473.44 1634.62 -8.854 < 2e-16 ***
## model_nameUlysse -12640.73 1511.18 -8.365 < 2e-16 ***
## model_nameUno -15394.83 2475.86 -6.218 5.09e-10 ***
## model_nameUp -9402.66 2737.16 -3.435 0.000593 ***
## model_nameUrban Cruiser -8313.24 2737.16 -3.037 0.002390 **
## model_nameUrvan -15132.33 4288.31 -3.529 0.000418 ***
## model_nameV220 9658.81 2304.90 4.191 2.79e-05 ***
## model_nameV250 25363.07 2304.90 11.004 < 2e-16 ***
## model_nameV40 -12007.79 1572.72 -7.635 2.31e-14 ***
## model_nameV50 -9395.11 1733.45 -5.420 6.00e-08 ***
## model_nameV60 -758.68 1917.79 -0.396 0.692400
## model_nameV70 -8912.42 1669.22 -5.339 9.39e-08 ***
## model_nameV8 -10632.33 4288.31 -2.479 0.013166 *
## model_nameV90 28167.67 3196.32 8.813 < 2e-16 ***
## model_nameVan -5387.33 3196.32 -1.685 0.091905 .
## model_nameVaneo -12757.47 1791.89 -7.120 1.10e-12 ***
## model_nameVanette -15088.63 1642.30 -9.188 < 2e-16 ***
## model_nameVario -7754.66 1964.57 -3.947 7.92e-05 ***
## model_nameVectra -13750.34 1439.52 -9.552 < 2e-16 ***
## model_nameVel Satis -12046.24 1634.62 -7.369 1.75e-13 ***
## model_nameVenga -9791.49 2092.48 -4.679 2.89e-06 ***
## model_nameVento -15074.16 1518.86 -9.925 < 2e-16 ***
## model_nameVenture -14182.33 3196.32 -4.437 9.14e-06 ***
## model_nameVenza 1591.47 1770.04 0.899 0.368596
## model_nameVerona -12749.33 2737.16 -4.658 3.21e-06 ***
## model_nameVersa -10982.33 3196.32 -3.436 0.000591 ***
## model_nameVerso -5631.15 1614.61 -3.488 0.000488 ***
## model_nameVesta -5856.97 1518.86 -3.856 0.000115 ***
## model_nameViano -2157.55 1627.48 -1.326 0.184949
## model_nameVibe -11161.94 1659.52 -6.726 1.77e-11 ***
## model_nameVIS -14687.91 4288.31 -3.425 0.000615 ***
## model_nameVision -15682.33 4288.31 -3.657 0.000256 ***
## model_nameVitara -8148.58 2092.48 -3.894 9.87e-05 ***
## model_nameVito -7847.30 1476.70 -5.314 1.08e-07 ***
## model_nameVivaro -6115.45 1603.31 -3.814 0.000137 ***
## model_nameVolt -968.18 2092.48 -0.463 0.643585
## model_nameVoyager -12850.88 1485.51 -8.651 < 2e-16 ***
## model_nameWagon R -15006.63 1845.39 -8.132 4.35e-16 ***
## model_nameWindstar -14392.33 2304.90 -6.244 4.30e-10 ***
## model_nameWrangler 2208.44 2304.90 0.958 0.337992
## model_nameWRX 8267.67 4288.31 1.928 0.053868 .
## model_nameX-Trail -3828.54 1491.61 -2.567 0.010271 *
## model_nameX-Type -11632.33 1845.39 -6.303 2.94e-10 ***
## model_nameX1 -762.71 1553.96 -0.491 0.623558
## model_nameX2 19411.92 4288.31 4.527 6.01e-06 ***
## model_nameX3 -986.21 1500.07 -0.657 0.510899
## model_nameX4 16360.17 2021.53 8.093 6.00e-16 ***
## model_nameX5 -1387.58 1448.56 -0.958 0.338118
## model_nameX5 M 10200.55 2092.48 4.875 1.09e-06 ***
## model_nameX50 -8615.83 2183.50 -3.946 7.97e-05 ***
## model_nameX6 8297.36 1495.19 5.549 2.89e-08 ***
## model_nameX6 M 6339.60 2183.50 2.903 0.003693 **
## model_nameX60 -7965.45 1669.22 -4.772 1.83e-06 ***
## model_nameX70 -2540.99 2183.50 -1.164 0.244543
## model_nameXantia -15218.38 1445.50 -10.528 < 2e-16 ***
## model_nameXC60 2199.76 1541.72 1.427 0.153638
## model_nameXC70 -4462.43 1620.83 -2.753 0.005905 **
## model_nameXC90 -3799.53 1476.70 -2.573 0.010086 *
## model_nameXE 10273.17 2183.50 4.705 2.55e-06 ***
## model_nameXedos 6 -15333.77 1603.31 -9.564 < 2e-16 ***
## model_nameXedos 9 -14180.86 1770.04 -8.012 1.16e-15 ***
## model_nameXF 29.21 1816.78 0.016 0.987171
## model_nameXG 30 -13932.33 4288.31 -3.249 0.001160 **
## model_nameXJ 3921.21 1816.78 2.158 0.030909 *
## model_nameXKR 367.67 4288.31 0.086 0.931674
## model_nameXL7 -9757.58 2475.86 -3.941 8.13e-05 ***
## model_nameXM -15381.29 1733.45 -8.873 < 2e-16 ***
## model_nameXRAY -6421.63 1733.45 -3.705 0.000212 ***
## model_nameXsara -14390.09 1469.15 -9.795 < 2e-16 ***
## model_nameXsara Picasso -12886.03 1496.74 -8.609 < 2e-16 ***
## model_nameXV -1678.23 1964.57 -0.854 0.392973
## model_nameYaris -11486.80 1477.10 -7.777 7.64e-15 ***
## model_nameYeti -6783.53 1598.16 -4.245 2.20e-05 ***
## model_nameYpsilon -14692.83 2737.16 -5.368 8.01e-08 ***
## model_nameZ3 -9382.33 4288.31 -2.188 0.028684 *
## model_nameZ4 -6832.33 4288.31 -1.593 0.111114
## model_nameZafira -10268.13 1445.41 -7.104 1.23e-12 ***
## model_nameZDX 1504.35 2021.53 0.744 0.456780
## model_nameZeta -13006.96 1917.79 -6.782 1.20e-11 ***
## model_nameZX -15832.03 1634.62 -9.685 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4043 on 37372 degrees of freedom
## Multiple R-squared: 0.6154, Adjusted R-squared: 0.6039
## F-statistic: 53.54 on 1117 and 37372 DF, p-value: < 2.2e-16The R2 is 0.6039 which indicates that a relatively large proportion of prices in the data can be explained by the model. Although more attributes will aid in predicting prices we can confirm that there is significant usefulness in using the model name attribute in our future models.
We continue by assessing the error of our model to get a better picture of the models accuracy.
confint(modelPrice)## 2.5 % 97.5 %
## (Intercept) 13830.59040 19434.05960
## model_name<U+0410>22 -4212.92831 6516.89831
## model_name<U+041C>20 -15109.29196 -6549.85138
## model_name<U+041C>5 1389.67833 11095.17167
## model_name100 -17904.86094 -12241.29920
## model_name1007 -16428.71196 -7869.27138
## model_name100NX -20610.25667 -10904.76333
## model_name106 -18606.62805 -11582.30767
## model_name107 -15916.48631 -8682.43869
## model_name11 -22472.19402 -9942.45598
## model_name110 -21822.19402 -9292.45598
## model_name111 -20931.50917 -11226.01583
## model_name1111 -19147.82167 -9442.32833
## model_name1119 -24537.52879 -7727.12121
## model_name112 -23946.75879 -7136.35121
## model_name116 -10270.59298 -3832.69702
## model_name118 -9838.31903 -2899.66430
## model_name12 -23519.93879 -6709.53121
## model_name120 -10946.24641 -3428.40359
## model_name121 -20033.78736 -11831.14836
## model_name125 -24387.52879 -7577.12121
## model_name1300 -23537.52879 -6727.12121
## model_name145 -18945.51233 -11921.19195
## model_name146 -18708.58801 -12300.77583
## model_name147 -16766.84881 -9532.80119
## model_name1500 1714.92833 11420.42167
## model_name155 -20012.35878 -11809.71979
## model_name156 -17284.94228 -11445.43503
## model_name159 -12194.70107 -4270.19893
## model_name164 -20189.98629 -11154.66371
## model_name166 -16726.23121 -10396.88500
## model_name180 -22737.52879 -5927.12121
## model_name19 -18969.31481 -13143.84065
## model_name190 -18277.31049 -12333.93389
## model_name2 -15201.77090 -8177.45053
## model_name200 -18781.24641 -11263.40359
## model_name200-Series -18431.28515 -12404.33073
## model_name2008 -9870.79629 -835.47371
## model_name200SX -7470.18629 1565.13629
## model_name205 -20531.69429 -11496.37171
## model_name206 -16502.04172 -10740.69743
## model_name207 -14503.37787 -8604.78997
## model_name208 -11892.44641 -4374.60359
## model_name21 -15419.14044 -9375.53120
## model_name2101 -18711.77707 -12382.43086
## model_name21011 -19326.34641 -11808.50359
## model_name21013 -19931.03982 -12006.53768
## model_name2102 -19959.04529 -11399.60471
## model_name2103 -18155.94641 -10638.10359
## model_name2104 -18927.91830 -11806.03324
## model_name2105 -18791.08200 -12580.14400
## model_name2106 -18776.48182 -12926.58976
## model_name2107 -18686.96531 -12651.84429
## model_name2108 -19015.03346 -12769.17775
## model_name2109 -18837.36574 -12793.75650
## model_name21099 -19265.65236 -12326.99764
## model_name2110 -18969.55662 -11945.23624
## model_name2111 -22247.19402 -9717.45598
## model_name2112 -19715.85857 -11791.35643
## model_name2113 -21247.19402 -8717.45598
## model_name2114 -18434.38447 -11410.06410
## model_name2115 -19028.16202 -11663.76071
## model_name2120 -23837.52879 -7027.12121
## model_name2121 -16554.89155 -10225.54534
## model_name21213 -17921.94089 -10220.70911
## model_name21214 -15891.18141 -8373.33859
## model_name2123 -21037.52879 -4227.12121
## model_name2125 -22679.69402 -10149.95598
## model_name2131 -17316.84741 -9799.00459
## model_name2140 -19266.81725 -12681.96037
## model_name2141 -20171.33696 -11611.89638
## model_name216 -5502.61657 1861.78475
## model_name218 -4933.90497 5795.92164
## model_name22 -23137.52879 -6327.12121
## model_name220 -1837.52879 14972.87879
## model_name2206 -24037.52879 -7227.12121
## model_name225 -16947.19402 -4417.45598
## model_name235 7512.47121 24322.87879
## model_name24 -16690.68947 -9666.36910
## model_name240 -18495.37167 -8789.87833
## model_name2401 -21852.19402 -9322.45598
## model_name2402 -22337.52879 -5527.12121
## model_name2410 -18641.82196 -11779.00179
## model_name244 -23787.52879 -6977.12121
## model_name25 -18411.84814 -11677.31630
## model_name264 -24037.52879 -7227.12121
## model_name2705 -17606.25570 -10667.60097
## model_name2752 -21197.19402 -8667.45598
## model_name2757 -22037.52879 -5227.12121
## model_name3 -12636.86082 -6887.82273
## model_name300 -11870.44845 -5767.94759
## model_name3000GT -14012.21196 -5452.77138
## model_name3008 -9899.07566 -4022.10759
## model_name301 -12842.21593 -4639.57693
## model_name306 -17927.05382 -12032.30365
## model_name307 -15234.33157 -9510.34387
## model_name308 -13336.51914 -7607.81730
## model_name309 -20863.90497 -10134.07836
## model_name3102 -21341.50902 -8811.77098
## model_name310210 -24087.52879 -7277.12121
## model_name310221 -23637.52879 -6827.12121
## model_name31029 -19724.93021 -11522.29122
## model_name3110 -18914.89341 -12371.45113
## model_name31105 -19459.70196 -10900.26138
## model_name315 -13058.07607 -5133.57393
## model_name3151 -19509.80417 -9804.31083
## model_name31512 -19110.32167 -9404.82833
## model_name31514 -19847.23831 -9117.41169
## model_name316 -16709.62814 -10896.61157
## model_name318 -14892.40659 -9171.57452
## model_name320 -11115.13984 -5416.68982
## model_name322 -18047.19402 -5517.45598
## model_name3221 -18470.95441 -10953.11159
## model_name322132 -23787.52879 -6977.12121
## model_name322133 -20633.64831 -9903.82169
## model_name323 -17868.72105 -12169.86680
## model_name3234 -24089.02879 -7278.62121
## model_name324 -19374.09881 -12140.05119
## model_name325 -13707.31323 -7688.22177
## model_name328 -11392.00169 -4712.22305
## model_name33 -24037.52879 -7227.12121
## model_name330 -11188.17090 -4163.85053
## model_name3302 -16442.04758 -9579.22742
## model_name3303 -19464.09229 -10428.76971
## model_name335 -7725.32607 199.17607
## model_name340 -19212.62229 -10177.29971
## model_name3500 -15137.52879 1672.87879
## model_name350Z -11897.19402 632.54402
## model_name360 -24437.52879 -7627.12121
## model_name3741 -24658.12879 -7847.72121
## model_name39099 -17867.75747 -10503.35616
## model_name3962 -20638.39902 -8108.66098
## model_name400 -24487.52879 -7677.12121
## model_name400-Series -18159.11696 -12239.71536
## model_name4007 -10200.88929 -2836.48798
## model_name4008 -9038.52879 7771.87879
## model_name401 -19747.19402 -7217.45598
## model_name402 -22222.19402 -9692.45598
## model_name403 -24847.82879 -8037.42121
## model_name405 -18762.42317 -12851.68992
## model_name406 -16829.59910 -11172.37837
## model_name407 -14380.93824 -8580.79658
## model_name408 -12377.08498 -6131.22926
## model_name410 -23662.20879 -6851.80121
## model_name412 -18861.61090 -11837.29053
## model_name420 1536.09503 12265.92164
## model_name423 Kombi -24888.52879 -8078.12121
## model_name428 4249.04893 12173.55107
## model_name435 11602.42833 21307.92167
## model_name440 -19452.54376 -12428.22338
## model_name45 -17726.96773 -11142.11084
## model_name451 -21897.19402 -9367.45598
## model_name452 -19997.57167 -10292.07833
## model_name460 -18870.41725 -12585.37146
## model_name469 -18153.45667 -11358.25215
## model_name4Runner -10190.18629 -1154.86371
## model_name4x4 -16209.18805 -9184.86767
## model_name5 -12536.49224 -6128.68007
## model_name500 -10169.64450 -1967.00550
## model_name5008 -7928.97045 -1664.10143
## model_name500L -10447.57167 -742.07833
## model_name500X -11137.52879 5672.87879
## model_name508 -7543.52703 -604.87230
## model_name518 -17257.46403 -10787.13513
## model_name520 -13003.15562 -7322.39354
## model_name523 -14777.08003 -8843.71755
## model_name524 -18636.84881 -11402.80119
## model_name525 -13789.08485 -8122.47598
## model_name528 -10694.05832 -4622.85255
## model_name530 -9114.44929 -3351.98488
## model_name535 -6286.70582 298.15106
## model_name539 -15097.57164 -4367.74503
## model_name540 -16785.32167 -7079.82833
## model_name545 -12547.19402 -17.45598
## model_name550 -4697.23831 6032.58831
## model_name6 -11388.18886 -5702.00716
## model_name600-Series -18289.79370 -12278.27819
## model_name605 -18110.66187 -12291.59637
## model_name607 -14989.41407 -9122.10942
## model_name620 -23037.52879 -6227.12121
## model_name626 -18092.23742 -12419.37277
## model_name630 -7160.18629 1875.13629
## model_name635 -4530.57164 6199.25497
## model_name640 11490.01371 20525.33629
## model_name645 -9782.20107 -1857.69893
## model_name650 -563.25982 7361.24232
## model_name69 -16883.78481 -10149.25297
## model_name725 -16267.25293 -8902.85162
## model_name728 -15741.25758 -9006.72575
## model_name730 -8976.59034 -3169.29243
## model_name732 -23537.52879 -6727.12121
## model_name735 -14833.88711 -8720.00098
## model_name740 -10206.41782 -4311.66765
## model_name745 -12478.75914 -6283.89086
## model_name75 -16440.39993 -10032.58776
## model_name750 -7177.25058 -966.31257
## model_name760 -10032.94089 -2331.70911
## model_name80 -17850.40119 -12173.68092
## model_name800-Series -20297.57167 -10592.07833
## model_name806 -16265.44770 -10281.65914
## model_name807 -13732.58920 -7504.68139
## model_name850 -17753.45667 -10958.25215
## model_name9 -23592.52879 -6782.12121
## model_name9-2X -19447.52879 -2637.12121
## model_name9 - 3 -14527.51115 -8456.30537
## model_name9 - 5 -15533.24247 -9338.37419
## model_name9 - 7X -13397.19402 -867.45598
## model_name90 -17713.32607 -9788.82393
## model_name900 -19785.07167 -10079.57833
## model_name9000 -18541.71695 -11861.93831
## model_name911 -11837.52879 4972.87879
## model_name929 -19662.46196 -11103.02138
## model_name940 -19162.74021 -10960.10122
## model_name960 -18859.98629 -9824.66371
## model_name965 -19163.90497 -8434.07836
## model_name966 -24437.52879 -7627.12121
## model_name968M -19726.62376 -12702.30338
## model_nameA1 -10941.76978 -3240.53799
## model_nameA13 -16391.24641 -8873.40359
## model_nameA140 -16994.36508 -10131.54492
## model_nameA150 -14250.82907 -7128.94401
## model_nameA160 -16866.62805 -9842.30767
## model_nameA170 -15993.84868 -9408.99180
## model_nameA180 -12599.71867 -4898.48688
## model_nameA190 -20447.23831 -9717.41169
## model_nameA2 -19537.52879 -2727.12121
## model_nameA200 3175.47171 12210.79429
## model_nameA210 -18297.23831 -7567.41169
## model_nameA3 -11793.04687 -5951.02639
## model_nameA4 -11874.59465 -6226.91594
## model_nameA4 Allroad -7550.18629 1485.13629
## model_nameA5 -4300.37452 1743.23472
## model_nameA6 -11655.12027 -6019.11977
## model_nameA6 Allroad -10486.71424 -4434.27576
## model_nameA7 53.90119 7287.94881
## model_nameA8 -9800.55210 -4054.29648
## model_nameAccent -14414.64875 -8636.02903
## model_nameAccord -14010.99845 -8313.74083
## model_nameActyon -10526.39108 -3896.27692
## model_nameAerio -20769.30879 -3958.90121
## model_nameAerodeck -19486.46667 -9780.97333
## model_nameAerostar -21137.52879 -4327.12121
## model_nameAgila -16470.97590 -9791.19726
## model_nameAlbea -18112.19402 -5582.45598
## model_nameAlero -24237.52879 -7427.12121
## model_nameAlhambra -13082.67862 -7144.38400
## model_nameAlmera -16399.38422 -10679.76860
## model_nameAlmera Tino -16421.05473 -10282.76177
## model_nameAlphard -337.52879 16472.87879
## model_nameAltea -12463.56659 -5920.12431
## model_nameAltima -13550.63137 -6428.74632
## model_nameAlto -17914.43475 -10550.03343
## model_nameAmarok -60.32167 9645.17167
## model_nameAMG GT4 -23537.52879 -6727.12121
## model_nameAmulet -19161.98629 -10126.66371
## model_nameAntara -8991.49397 -2662.14776
## model_nameArmada -11847.19402 682.54402
## model_nameArosa -19797.57167 -10092.07833
## model_nameAscona -19313.31379 -12633.53516
## model_nameAspen -12537.52879 4272.87879
## model_nameAstra -14930.66671 -9297.43120
## model_nameAstro Van -17787.52879 -977.12121
## model_nameASX -7991.00852 -1825.85201
## model_nameAtlas -1479.42645 6221.80534
## model_nameAtos -18441.01548 -11206.96785
## model_nameAuris -11283.89789 -5212.69211
## model_nameAvella -22410.06902 -9880.33098
## model_nameAvenger -14262.18629 -5226.86371
## model_nameAvensis -11890.20186 -6147.49481
## model_nameAvensis Verso -13909.52708 -7279.41292
## model_nameAveo -14737.46287 -8634.96201
## model_nameAviator -18637.52879 -1827.12121
## model_nameAX -21997.69402 -9467.95598
## model_nameAygo -14841.24641 -7323.40359
## model_nameAztek -16547.19402 -4017.45598
## model_nameB-Max -10385.32167 -679.82833
## model_nameB150 -18687.52879 -1877.12121
## model_nameB160 -9512.21196 -952.77138
## model_nameB170 -17747.19402 -5217.45598
## model_nameB180 -8751.20958 -2121.09542
## model_nameB200 -12403.14970 -5464.49497
## model_nameBaleno -18383.24736 -12231.85238
## model_nameBedford Blitz -24237.52879 -7427.12121
## model_nameBeetle -13128.71196 -4569.27138
## model_nameBerlingo -14850.66689 -8907.29029
## model_nameBesta -20747.19402 -8217.45598
## model_nameBipper -16022.57167 -6317.07833
## model_nameBipper Tepee -19237.52879 -2427.12121
## model_nameBlazer -17074.93021 -8872.29122
## model_nameBluebird -20258.82021 -12056.18122
## model_nameBonneville -23637.52879 -6827.12121
## model_nameBora -16106.72826 -9841.85924
## model_nameBoxer -12452.75118 -6481.45814
## model_nameBoxster -16037.52879 772.87879
## model_nameBrava -18380.36329 -12503.39521
## model_nameBravo -17773.64720 -11845.07355
## model_nameBreez -21377.19402 -8847.45598
## model_nameBrera -3037.52879 13772.87879
## model_nameBT-50 -16537.52879 272.87879
## model_nameBX -22078.63402 -9548.89598
## model_nameC-Crosser -10559.98629 -1524.66371
## model_nameC-ELYSÉE -12009.45667 -5214.25215
## model_nameC-Max -13127.49227 -7198.91862
## model_nameC1 -16420.37862 -7860.93804
## model_nameC1500 -17237.52879 -427.12121
## model_nameC180 -11695.26279 -5896.83234
## model_nameC2 -16855.27423 -9154.04244
## model_nameC200 -14078.01551 -8187.00449
## model_nameC220 -13518.10083 -7630.73410
## model_nameC230 -13707.88906 -7028.11042
## model_nameC240 -15575.82607 -7651.32393
## model_nameC25 -18647.57167 -8942.07833
## model_nameC250 -9191.44641 -1673.60359
## model_nameC270 -15947.19402 -3417.45598
## model_nameC280 -19322.57167 -9617.07833
## model_nameC3 -14605.22121 -8621.43265
## model_nameC3 Picasso -13364.40293 -6000.00162
## model_nameC30 -13211.77167 -3506.27833
## model_nameC300 -16597.19402 -4067.45598
## model_nameC320 -13630.57164 -2900.74503
## model_nameC350 -15537.52879 1272.87879
## model_nameC4 -13058.10139 -7288.56756
## model_nameC4 AirCross -12322.19402 207.54402
## model_nameC4 Grand Picasso -9667.30746 -3768.71957
## model_nameC4 Picasso -10994.60311 -4923.39733
## model_nameC5 -13793.62302 -8119.66717
## model_nameC6 -13437.57167 -3732.07833
## model_nameC63AMG 3962.47121 20772.87879
## model_nameC70 -16883.78736 -8681.14836
## model_nameC8 -13640.50032 -7232.68814
## model_nameCabstar -16537.52879 272.87879
## model_nameCaddy -11287.57230 -5363.65034
## model_nameCaliber -13133.60326 -6868.73424
## model_nameCalibra -18722.39020 -11357.98889
## model_nameCamaro 773.85359 8291.69641
## model_nameCampo -23437.52879 -6627.12121
## model_nameCamry -8724.42310 -2960.82255
## model_nameCaptiva -7919.36330 -1805.47717
## model_nameCaptur -6683.82756 1017.40423
## model_nameCaravan -16369.32491 -10492.35684
## model_nameCarens -15275.25067 -9047.34286
## model_nameCarina E -17759.24244 -11678.05112
## model_nameCarina II -20997.57167 -11292.07833
## model_nameCarisma -17845.83673 -12020.36257
## model_nameCarnival -16256.26010 -10091.10359
## model_nameCascada -2137.52879 14672.87879
## model_nameCatera -20297.19402 -7767.45598
## model_nameCavalier -20847.19402 -8317.45598
## model_nameCayenne -2685.73131 3333.36015
## model_nameCebrium -15233.69402 -2703.95598
## model_nameCee'd -10722.78122 -4935.54312
## model_nameCelica -15562.84881 -8328.80119
## model_nameCerato -11525.10258 -5087.20662
## model_nameChallenger 462.47121 17272.87879
## model_nameChance -19794.44831 -9064.62169
## model_nameCharger -1137.52879 15672.87879
## model_nameChaser -18597.19402 -6067.45598
## model_nameCherokee -12481.44641 -4963.60359
## model_nameCinquecento -21330.57164 -10600.74503
## model_nameCirrus -18576.50164 -10373.86264
## model_nameCitan -7791.98629 1243.33629
## model_nameCitigo -19537.52879 -2727.12121
## model_nameCity -16147.19402 -3617.45598
## model_nameCivic -14206.76855 -8526.27211
## model_nameCK -18911.51441 -11393.67159
## model_nameCL -22687.52879 -5877.12121
## model_nameCL420 -18037.52879 -1227.12121
## model_nameCL500 -8257.07607 -332.57393
## model_nameCL550 -8537.52879 8272.87879
## model_nameCLA180 -3447.69402 9082.04402
## model_nameCLA200 1946.91355 9648.14534
## model_nameCLA220 -3137.52879 13672.87879
## model_nameCLA250 862.47121 17672.87879
## model_nameCLA45 AMG 13302.76169 24032.58831
## model_nameClarus -18624.09799 -12580.48875
## model_nameClio -16339.23350 -10515.93750
## model_nameCLK200 -15158.51548 -7924.46785
## model_nameCLK230 -16160.07167 -6454.57833
## model_nameCLK240 -18537.52879 -1727.12121
## model_nameCLK270 -15747.19402 -3217.45598
## model_nameCLK280 -15937.52879 872.87879
## model_nameCLK320 -13712.21593 -5509.57693
## model_nameCLS250 10013.76169 20743.58831
## model_nameCLS320 -7647.19402 4882.54402
## model_nameCLS350 -6448.65569 94.78659
## model_nameCLS400 17769.42836 28499.25497
## model_nameCLS500 -5797.23831 4932.58831
## model_nameCLS55 AMG -8137.52879 8672.87879
## model_nameCLS550 -10937.52879 5872.87879
## model_nameCLS63 AMG 24862.47121 41672.87879
## model_nameClubman -11330.57164 -600.74503
## model_nameColorado -13487.52879 3322.87879
## model_nameColt -17186.77854 -11284.25037
## model_nameCombo -15780.73508 -8917.91492
## model_nameCommander -10552.80167 -847.30833
## model_nameCommodore -24037.52879 -7227.12121
## model_nameCompass -11795.87862 -3236.43804
## model_nameConcerto -22422.19402 -9892.45598
## model_nameConcorde -18032.07607 -10107.57393
## model_nameContinental -22237.52879 -5427.12121
## model_nameContour -19255.21593 -11052.57693
## model_nameCooper -7541.05653 -1070.72763
## model_nameCooper S -9442.31570 -2503.66097
## model_nameCordoba -17730.85890 -11639.24565
## model_nameCorolla -14347.75169 -8609.23977
## model_nameCorolla Verso -12490.31334 -6387.81248
## model_nameCorrado -20537.52879 -3727.12121
## model_nameCorsa -15687.88676 -9932.94516
## model_nameCougar -17746.24641 -10228.40359
## model_nameCountryman -1107.18836 5831.46636
## model_nameCoupe -16751.10629 -10540.16828
## model_nameCourier -19627.03978 -11925.80799
## model_nameCR-V -8563.54361 -2721.52313
## model_nameCR-Z -12260.07167 -2554.57833
## model_nameCrafter -2575.82938 3526.67148
## model_nameCreta -3679.07308 2951.04108
## model_nameCroma -19535.08111 -12170.67980
## model_nameCross Polo -18737.52879 -1927.12121
## model_nameCrosstour -1930.57164 8799.25497
## model_nameCrown -24787.52879 -7977.12121
## model_nameCrown Victoria -21087.52879 -4277.12121
## model_nameCruze -10784.64531 -5016.33305
## model_nameCRX -23537.52879 -6727.12121
## model_nameCT -4632.88929 2731.51202
## model_nameCTS -13266.35878 -5063.71979
## model_nameCX -21037.52879 -4227.12121
## model_nameCX-3 -5197.19402 7332.54402
## model_nameCX-5 -1241.67592 5064.82059
## model_nameCX-7 -11255.89684 -5345.16359
## model_nameCX-9 4249.98143 12174.48357
## model_nameDaily -9461.24008 -3698.77567
## model_nameDakota -19537.52879 -2727.12121
## model_nameDart -10469.98629 -1434.66371
## model_nameDe Ville -13710.07167 -4004.57833
## model_nameDedra -19362.21593 -11159.57693
## model_nameDeer -18197.23831 -7467.41169
## model_nameDefender 107.78333 9813.27667
## model_nameDelta -15786.39641 -8268.55359
## model_nameDemio -18903.71196 -10344.27138
## model_nameDiscovery -4178.13604 2151.21017
## model_nameDiscovery Sport 12549.81371 21585.13629
## model_nameDoblo -11329.26221 -5267.63247
## model_nameDokker -12041.24641 -4523.40359
## model_nameDS4 -11411.98629 -2376.66371
## model_nameDS5 -7399.98629 1635.33629
## model_nameDucato -14154.26219 -8255.67430
## model_nameDurango -10128.71196 -1569.27138
## model_nameDuster -9234.69013 -3390.10229
## model_nameE200 -12771.52083 -7033.81778
## model_nameE220 -13089.74928 -7304.00601
## model_nameE2200 -20322.57167 -10617.07833
## model_nameE230 -16508.58455 -10446.95481
## model_nameE240 -16667.92493 -10260.11276
## model_nameE250 -11370.56664 -5256.68051
## model_nameE260 -18271.29758 -11408.47742
## model_nameE270 -13759.35298 -7321.45702
## model_nameE280 -13098.16609 -6718.33576
## model_nameE290 -17320.37862 -8760.93804
## model_nameE300 -12264.83276 -5885.00243
## model_nameE320 -13115.73992 -6809.24341
## model_nameE350 -3831.26224 2498.08397
## model_nameE400 14852.80598 27382.54402
## model_nameE420 -15764.23831 -5034.41169
## model_nameE430 -18847.19402 -6317.45598
## model_nameE450 -15537.52879 1272.87879
## model_nameE50 AMG -20037.52879 -3227.12121
## model_nameE500 -11897.19402 632.54402
## model_nameE63 AMG 11452.80598 23982.54402
## model_nameEC7 -16349.98629 -7314.66371
## model_nameEcho -18622.57167 -8917.07833
## model_nameEclipse -16314.06020 -9518.85568
## model_nameEclipse Cross -488.72902 12041.00902
## model_nameEconoline -16126.03829 -7090.71571
## model_nameEcoSport -6946.46141 571.38141
## model_nameEdge 4604.45471 13163.89529
## model_nameElantra -13823.35697 -7845.92062
## model_nameEldorado -14147.19402 -1617.45598
## model_nameElement -11815.41141 -4297.56859
## model_nameELR 2465.57583 12171.06917
## model_nameElysion -13687.52879 3122.87879
## model_nameEmgrand -11164.50534 -3463.27355
## model_nameEmgrand 7 -10961.12196 -2401.68138
## model_nameEnclave -4537.52879 12272.87879
## model_nameEncore -7278.04648 -1139.75352
## model_nameEndeavor -17738.52879 -928.12121
## model_nameEos -13730.57164 -3000.74503
## model_nameEpica -14110.94089 -6409.70911
## model_nameEquinox -9482.07607 -1557.57393
## model_nameEquus -7547.57164 3182.25497
## model_nameES -4469.31314 2265.21870
## model_nameEscalade -6044.74676 1077.13830
## model_nameEscape -8310.48504 -2443.18039
## model_nameEscort -18711.01406 -12992.58993
## model_nameEspace -13854.38346 -8039.38866
## model_nameEspero -19381.43958 -12701.66095
## model_nameEvasion -16162.22539 -10223.93077
## model_nameEX -7711.24641 -193.40359
## model_nameEX7 -9821.57534 -2120.34355
## model_nameExcursion 9962.47121 26772.87879
## model_nameExpedition -16097.23831 -5367.41169
## model_nameExpert -13665.06748 -6985.28884
## model_nameExpert Tepee -12524.04164 -1794.21503
## model_nameExplorer -9160.10595 -2875.06017
## model_nameExpress 1604.62138 10164.06196
## model_nameF-Pace 17714.92833 27420.42167
## model_nameF-Type 24962.47121 41772.87879
## model_nameF150 -1163.90497 9565.92164
## model_nameF250 -5764.23831 4965.58831
## model_nameFabia -14469.69325 -8683.94998
## model_nameFavorit -22472.19402 -9942.45598
## model_nameFelicia -18764.19068 -12638.38663
## model_nameFiesta -16564.01370 -10790.70399
## model_nameFiorino -17535.52862 -8976.08804
## model_nameFirebird -15997.23831 -5267.41169
## model_nameFit -10871.40978 -4327.96750
## model_nameFJ Cruiser -9037.52879 7772.87879
## model_nameFlorid -21037.52879 -4227.12121
## model_nameFluence -11426.10664 -5072.36979
## model_nameFocus -13719.11804 -8056.97217
## model_nameFora -18707.49829 -9672.17571
## model_nameForenza -18872.19402 -6342.45598
## model_nameForester -11315.78104 -5438.81296
## model_nameForza -17875.20417 -8169.71083
## model_nameFox -16477.49645 -8776.26466
## model_nameFR-V -13728.87862 -5169.43804
## model_nameFreelander -12736.28114 -6674.65141
## model_nameFreemont -8287.52879 8522.87879
## model_nameFreestyle -19197.19402 -6667.45598
## model_nameFrontera -15736.48202 -9665.27624
## model_nameFrontier -8037.52879 8772.87879
## model_nameFusion -9870.26403 -4111.11329
## model_nameFX -7006.62154 -1129.65346
## model_nameG -9880.21432 -3200.43568
## model_nameG270 462.47121 17272.87879
## model_nameG300 -11530.57164 -800.74503
## model_nameG320 2502.80598 15032.54402
## model_nameG350 -15837.52879 972.87879
## model_nameG400 -76.26497 10653.56164
## model_nameG500 -2397.19402 10132.54402
## model_nameG55 AMG 8352.80598 20882.54402
## model_nameG6 -19197.19402 -6667.45598
## model_nameGalant -17443.70619 -11662.30494
## model_nameGalaxy -13867.12361 -8142.48431
## model_nameGalloper -17628.71196 -9069.27138
## model_nameGenesis -11422.69402 1107.04402
## model_nameGentra -18337.52879 -1527.12121
## model_nameGetz -16337.70582 -9752.84894
## model_nameGiulietta -13897.19402 -1367.45598
## model_nameGL320 -2532.05978 5169.17201
## model_nameGL350 6651.31386 13194.75614
## model_nameGL400 15727.80598 28257.54402
## model_nameGL420 -9787.52879 7022.87879
## model_nameGL450 -7066.34445 55.54060
## model_nameGL500 8016.94799 15718.17978
## model_nameGL550 -9313.90497 1415.92164
## model_nameGL63 25602.30598 38132.04402
## model_nameGLA200 3671.82638 12231.26696
## model_nameGLA250 6612.97503 17342.80164
## model_nameGLA45 AMG 15462.47121 32272.87879
## model_nameGLC200 24378.87121 41189.27879
## model_nameGLC250 15821.24833 25526.74167
## model_nameGLC300 10862.47121 27672.87879
## model_nameGLE300 19477.80598 32007.54402
## model_nameGLE350 20509.53598 33039.27402
## model_nameGLK220 -1657.50229 7377.82029
## model_nameGLK250 -3787.52879 13022.87879
## model_nameGLK300 -3730.57164 6999.25497
## model_nameGLK350 -5499.83607 2424.66607
## model_nameGol -22837.52879 -6027.12121
## model_nameGolf -15844.73793 -10209.65512
## model_nameGolf Plus -13044.07482 -6779.20580
## model_nameGran Turismo -571.43522 6550.44984
## model_nameGranada -20375.33629 -11340.01371
## model_nameGrand Am -23737.52879 -6927.12121
## model_nameGrand C-Max -9647.23831 1082.58831
## model_nameGrand Caravan -11895.79944 -5834.16971
## model_nameGrand Cherokee -9566.70798 -3651.70088
## model_nameGrand Espace -12607.39757 -6227.56724
## model_nameGrand Modus -19137.52879 -2327.12121
## model_nameGrand Scenic -10645.32961 -4826.26411
## model_nameGrand Starex -7977.69867 -276.46688
## model_nameGrand Vitara -11268.49333 -5344.57137
## model_nameGrand Voyager -14248.19116 -8134.30503
## model_nameGrande Punto -15052.22914 -8698.49229
## model_nameGrandeur -11648.07167 -1942.57833
## model_nameGrandis -12051.03676 -4929.15170
## model_nameGrandland X -6042.52879 10767.87879
## model_nameGranta -14428.24133 -7565.42117
## model_nameGS -6369.79121 -40.44500
## model_nameGT -15917.19402 -3387.45598
## model_nameGTV -17614.66917 -7909.17583
## model_nameGX 412.78804 8972.22862
## model_nameH-1 -13357.67258 -6494.85242
## model_nameH 100 -19860.07167 -10154.57833
## model_nameH3 -11205.43475 -3841.03343
## model_nameH5 -15397.19402 -2867.45598
## model_nameH6 -15737.52879 1072.87879
## model_nameHd -7997.23831 2732.58831
## model_nameHHR -14410.32167 -4704.82833
## model_nameHiAce -17871.57164 -7141.74503
## model_nameHighlander 52.89193 6406.62878
## model_nameHilux -690.78736 7511.85164
## model_nameHover -14033.16312 -6331.93133
## model_nameHR-V -14915.07131 -7681.02369
## model_nameHunter -16029.98629 -6994.66371
## model_nameI -17397.19402 -4867.45598
## model_namei10 -16391.13357 -8466.63143
## model_namei20 -14588.81519 -7564.49481
## model_namei3 9477.30598 22007.04402
## model_namei30 -12008.11031 -5905.60945
## model_namei40 -6655.42340 -247.61122
## model_nameIbiza -16067.18788 -10083.39931
## model_nameIdea -17069.98629 -8034.66371
## model_nameIgnis -17419.98629 -8384.66371
## model_nameILX -5286.57164 5443.25497
## model_nameImpala -23137.52879 -6327.12121
## model_nameImpreza -12825.84021 -6711.95408
## model_nameInsight -11814.47748 -5134.69884
## model_nameInsignia -8613.78019 -2828.03692
## model_nameIntegra -17897.19402 -5367.45598
## model_nameInterstar -14980.57164 -4250.74503
## model_nameIntrepid -17330.72000 -10825.32131
## model_nameIQ -15330.57164 -4600.74503
## model_nameIS -6710.64164 -356.90479
## model_nameix20 -17837.52879 -1027.12121
## model_nameix35 -6373.85063 -208.69411
## model_nameix55 -8147.69402 4382.04402
## model_nameJ5 -18758.01111 -11393.60980
## model_nameJazz -14934.37199 -8580.63515
## model_nameJetta -14545.80799 -8848.55037
## model_nameJimny -17647.69402 -5117.95598
## model_nameJohn Cooper Works -1937.52879 14872.87879
## model_nameJoice -17995.54529 -9436.10471
## model_nameJourney -8927.38534 -1226.15355
## model_nameJuke -8449.71205 -2501.09668
## model_nameJumper -13589.54829 -7562.59387
## model_nameJumpy -12643.01946 -5780.19929
## model_nameJusty -16392.87862 -7833.43804
## model_nameJX 1564.92833 11270.42167
## model_nameKa -17923.70203 -11189.17019
## model_nameKadett -19323.11420 -13058.24518
## model_nameKadjar -5269.57607 2654.92607
## model_nameKalina -15704.15534 -8002.92355
## model_nameKalos -18497.57167 -8792.07833
## model_nameKangoo -15925.78334 -9823.28248
## model_nameKapitan -17037.52879 -227.12121
## model_nameKappa -18030.30778 -11592.41182
## model_nameKaptur -5328.57630 1256.28059
## model_nameKimo -19464.23831 -8734.41169
## model_nameKodiaq 15739.13308 21519.13260
## model_nameKoleos -8204.95162 -1180.63124
## model_nameKorando -17347.19402 -4817.45598
## model_nameKuga -5618.17052 711.17569
## model_nameKyron -11993.11008 -5130.28992
## model_nameL200 -6496.18570 442.46903
## model_nameL300 -21497.19402 -8967.45598
## model_nameL400 -19163.90497 -8434.07836
## model_nameL50 -18037.52879 -1227.12121
## model_nameLacetti -15524.53646 -9144.70613
## model_nameLaCrosse -4037.52879 12772.87879
## model_nameLaguna -16499.75123 -10855.52899
## model_nameLancer -15194.61082 -9437.59349
## model_nameLancer Evolution -7699.98629 1335.33629
## model_nameLancia -24887.52879 -8077.12121
## model_nameLand Cruiser 2645.36384 8480.00026
## model_nameLanos -18353.21528 -12282.00950
## model_nameLantra -18594.46980 -12634.87659
## model_nameLargus -13131.06069 -6587.61841
## model_nameLatitude -11910.32167 -2204.82833
## model_nameLC -15524.62429 -6489.30171
## model_nameLe Baron -23537.52879 -6727.12121
## model_nameLeaf -7939.98629 1095.33629
## model_nameLegacy -15175.37080 -9231.99420
## model_nameLeganza -18324.04478 -11780.60250
## model_nameLegend -15032.27090 -8007.95053
## model_nameLeMans -22537.52879 -5727.12121
## model_nameLeon -12787.58208 -6157.46792
## model_nameLeone -23537.52879 -6727.12121
## model_nameLHS -17563.07607 -9638.57393
## model_nameLiana -16542.98425 -9808.45242
## model_nameLibero -21430.13402 -8900.39598
## model_nameLiberty -15435.07167 -5729.57833
## model_nameLinea -15864.76423 -8163.53244
## model_nameLodgy -10229.78736 -2027.14836
## model_nameLogan -13767.38063 -7980.14253
## model_nameLogo -22837.52879 -6027.12121
## model_nameLS -6609.39785 185.80667
## model_nameLT -15146.22986 -9320.75570
## model_nameLuidor -4447.19402 8082.54402
## model_nameLumina -19486.23831 -8756.41169
## model_nameLupo -17716.27423 -10015.04244
## model_nameLX 11805.42393 19729.92607
## model_nameLybra -17858.08678 -11273.22989
## model_nameM -9083.10373 -2287.89921
## model_nameM2 14862.47121 31672.87879
## model_nameM3 -771.34667 8934.14667
## model_nameM4 -18137.52879 -1327.12121
## model_nameM6 -11237.52879 5572.87879
## model_nameMacan 18769.42836 29499.25497
## model_nameMagentis -14532.58821 -7669.76804
## model_nameMagnum -15797.19402 -3267.45598
## model_nameMalaga -22615.48902 -10085.75098
## model_nameMalibu -7434.20499 -1149.15921
## model_nameManager -20872.19402 -8342.45598
## model_nameMarea -18309.23413 -12494.23933
## model_nameMark VIII -17597.19402 -5067.45598
## model_nameMascott -10645.37862 -2085.93804
## model_nameMaster -9972.64743 -3761.70942
## model_nameMatiz -17703.35724 -11712.99669
## model_nameMatrix -15625.93775 -9431.06947
## model_nameMaverick -16747.45107 -8822.94893
## model_nameMaxima -16665.73992 -10359.24341
## model_nameMaxity -24137.52879 -7327.12121
## model_nameMB100 -18435.70582 -11850.84894
## model_nameMDX -4179.61059 2405.24630
## model_nameMegane -15746.77002 -10084.15967
## model_nameMegane Scenic -16855.31577 -11092.85136
## model_nameMeriva -14094.38183 -8233.14864
## model_nameMicra -16987.64244 -10759.73462
## model_nameMii -16672.69402 -4142.95598
## model_nameMillenia -17074.68449 -10720.94765
## model_nameMirage -19517.57167 -9812.07833
## model_nameMK -17802.45107 -9877.94893
## model_nameMKC 2220.01371 11255.33629
## model_nameMKX -13537.52879 3272.87879
## model_nameMKZ -6110.07167 3595.42167
## model_nameML230 -16847.23831 -6117.41169
## model_nameML250 9912.47121 26722.87879
## model_nameML270 -12251.21432 -5571.43568
## model_nameML280 -8214.34497 2515.48164
## model_nameML300 4539.15264 12741.79164
## model_nameML320 -8798.96636 -2755.35711
## model_nameML350 -1603.73100 4298.79717
## model_nameML400 -12547.51376 -5523.19338
## model_nameML430 -15077.89164 -4348.06503
## model_nameML500 -4531.98629 4503.33629
## model_nameML55 AMG -17152.19402 -4622.45598
## model_nameML550 -7897.19402 4632.54402
## model_nameML63 AMG 11461.47121 28271.87879
## model_nameModel F -24037.52879 -7227.12121
## model_nameModus -17747.23831 -7017.41169
## model_nameMohave 1102.76169 11832.58831
## model_nameMokka -6881.98945 -411.66055
## model_nameMondeo -16233.09390 -10594.49289
## model_nameMonterey -18247.57167 -8542.07833
## model_nameMontero -13811.65236 -6872.99764
## model_nameMonza -23472.50879 -6662.10121
## model_nameMovano -12196.34384 -4831.94253
## model_nameMPV -15473.55534 -9454.46389
## model_nameMR2 -10037.52879 6772.87879
## model_nameMultipla -17149.19609 -10741.38391
## model_nameMurano -8878.57148 -2740.27852
## model_nameMusa -16487.82167 -6782.32833
## model_nameMusso -17339.16423 -9637.93244
## model_nameMustang -898.10870 5836.42314
## model_nameMX-3 -19438.71196 -10879.27138
## model_nameMX-6 -17930.57164 -7200.74503
## model_nameMyWay -11447.19402 1082.54402
## model_nameNavara -9523.17258 -2660.35242
## model_nameNavigator -14629.98629 -5594.66371
## model_nameNemo -16183.78736 -7981.14836
## model_nameNeon -17858.84332 -11787.63755
## model_nameNexia -18385.89535 -12006.06502
## model_nameNitro -12872.19402 -342.45598
## model_nameNiva -15128.16202 -7763.76071
## model_nameNote -13558.38387 -7178.55354
## model_nameNubira -18005.05431 -11969.93329
## model_nameNV S -12037.52879 4772.87879
## model_nameNV200 -17037.52879 -227.12121
## model_nameNX 15211.28133 22912.51312
## model_nameOctavia -8109.98367 -2445.60541
## model_nameOdyssey 742.63804 9302.07862
## model_nameOmega -17476.09353 -11815.00360
## model_nameOne -11788.46756 -4087.23577
## model_nameOptima -7217.73208 -587.61792
## model_nameOrion -19605.82667 -12810.62215
## model_nameOrlando -8762.34233 -1738.02195
## model_nameOutback -10722.81690 -4794.24325
## model_nameOutlander -8106.93717 -2289.92642
## model_namePaceman -5937.52879 10872.87879
## model_namePacifica -12216.85799 -6135.66667
## model_namePajero -9761.07912 -3846.07202
## model_namePajero Pinin -16575.07167 -6869.57833
## model_namePajero Sport -8381.62449 -2027.88765
## model_namePalio -18697.53992 -12391.04341
## model_namePanamera 6064.92833 15770.42167
## model_namePanda -16443.42137 -9321.53632
## model_namePartner -15211.44963 -9140.24385
## model_namePartner Tepee -12561.43215 -5327.38452
## model_namePaseo -20480.57164 -9750.74503
## model_namePassat -14337.19390 -8717.98459
## model_namePassat CC -8414.36591 -2449.01742
## model_namePassport -22571.43879 -5761.03121
## model_namePathfinder -7219.03831 -973.18260
## model_namePatriot -10849.87308 -4219.75892
## model_namePatrol -7531.37407 -1285.51836
## model_namePegasus -19537.52879 -2727.12121
## model_namePeri -19730.90497 -9001.07836
## model_namePhaeton -11188.41503 -4249.76030
## model_namePhedra -14790.07307 -6587.43407
## model_namePicanto -14921.85241 -7404.00959
## model_namePicnic -16315.95522 -9194.07016
## model_namePilot -5749.68215 1484.36548
## model_namePixo -17385.15164 -6655.32503
## model_namePointer -21787.52879 -4977.12121
## model_namePolo -15424.71579 -9665.56505
## model_namePolo Sedan -10430.02553 -4661.71328
## model_namePony -19558.70747 -12194.30616
## model_namePorter -19037.52879 -2227.12121
## model_namePrairie -20447.57167 -10742.07833
## model_namePregio -22537.52879 -5727.12121
## model_namePrelude -16720.68215 -9486.63452
## model_namePremacy -16540.01232 -10275.14330
## model_namePrevia -16135.87838 -8771.47707
## model_namePride -21197.23831 -10467.41169
## model_namePrimastar -14947.19402 -2417.45598
## model_namePrimera -17151.70124 -11483.82223
## model_namePriora -16317.75196 -9454.93179
## model_namePrius -6189.41046 -240.79510
## model_namePro Cee'd -17637.52879 -827.12121
## model_nameProbe -18698.97241 -11181.12959
## model_nameProtege -18723.97362 -10164.53304
## model_namePT Cruiser -15844.38493 -9436.57276
## model_namePulsar -13737.52879 3072.87879
## model_namePuma -20597.57164 -9867.74503
## model_namePunto -17594.79403 -11796.36358
## model_nameQ -1319.98629 7715.33629
## model_nameQ3 -2118.33208 4511.78208
## model_nameQ5 -1949.11201 3974.80995
## model_nameQ7 79.90768 5960.25624
## model_nameQashqai -7209.88771 -1467.18066
## model_nameQashqai+2 -8569.58274 -1889.80410
## model_nameQQ -18920.54529 -10361.10471
## model_nameQQ6 -20016.63164 -9286.80503
## model_nameQuest -14330.57164 -3600.74503
## model_nameQuoris -3964.57164 6765.25497
## model_nameQX 1052.60702 7490.50298
## model_nameR280 -11897.19402 632.54402
## model_nameR320 -11038.52879 5771.87879
## model_nameR350 -8328.59881 -1094.55119
## model_nameR500 -16137.52879 672.87879
## model_nameRam 1195.78804 9755.22862
## model_nameRange Rover -6894.32459 -768.52054
## model_nameRange Rover Evoque 4608.01792 11238.13208
## model_nameRange Rover Sport -67.25878 6097.89773
## model_nameRanger -9246.50164 -1043.86264
## model_nameRapid -4770.03635 938.17965
## model_nameRAV4 -6727.29353 -970.27620
## model_nameRDX -6722.57167 2982.92167
## model_nameRegal -6122.57167 3582.92167
## model_nameRekord -20261.58736 -12058.94836
## model_nameRendez Vous -19737.52879 -2927.12121
## model_nameRenegade 15466.09503 26195.92164
## model_nameRetona -17916.90497 -7187.07836
## model_nameRexton -12387.45028 -5917.12138
## model_nameRezzo -17334.05201 -9632.82022
## model_nameRidgeline -6447.19402 6082.54402
## model_nameRio -12130.13568 -6389.97642
## model_nameRL -16548.48879 261.91879
## model_nameRodius -12524.82736 -4322.18836
## model_nameRogue -10610.07167 -904.57833
## model_nameRoomster -13512.50032 -7104.68814
## model_nameRS6 -10687.57167 -982.07833
## model_nameRSX -16247.19402 -3717.45598
## model_nameRVR -15537.52879 1272.87879
## model_nameRX -2124.43560 3736.79760
## model_nameRX-8 -14147.93021 -5945.29122
## model_nameS-Coupe -24137.52879 -7327.12121
## model_nameS-Max -8659.36072 -2597.73098
## model_nameS-Type -16977.23831 -6247.41169
## model_nameS1000 -22950.83879 -6140.43121
## model_nameS2000 4962.47121 21772.87879
## model_nameS220 -13795.37862 -5235.93804
## model_nameS260 -22537.52879 -5727.12121
## model_nameS280 -24037.52879 -7227.12121
## model_nameS300 -13366.74373 -6571.53921
## model_nameS320 -12273.26551 -6238.14449
## model_nameS350 -4584.41942 1795.41091
## model_nameS4 -12707.07607 -4782.57393
## model_nameS40 -14251.09642 -8401.20436
## model_nameS400 -10763.32607 -2838.82393
## model_nameS420 -14227.19402 -1697.45598
## model_nameS430 -14681.24641 -7163.40359
## model_nameS450 -3397.23831 7332.58831
## model_nameS5 -3828.71196 4730.72862
## model_nameS500 -9040.93427 -3092.31891
## model_nameS550 -6383.35141 1134.49141
## model_nameS560 -21037.52879 -4227.12121
## model_nameS6 -14628.71196 -6069.27138
## model_nameS60 -11245.93668 -5428.92594
## model_nameS600 -9330.90497 1398.92164
## model_nameS63 AMG 852.80598 13382.54402
## model_nameS65 AMG 14352.80598 26882.54402
## model_nameS70 -17698.01566 -10333.61434
## model_nameS8 -8495.54529 63.89529
## model_nameS80 -11305.00260 -5520.73003
## model_nameS90 -18329.98629 -9294.66371
## model_nameSafe -17957.97917 -8252.48583
## model_nameSafrane -18059.26164 -12231.56571
## model_nameSamurai -17897.19402 -5367.45598
## model_nameSandero -11404.19981 -5571.92415
## model_nameSanta Fe -6946.00110 -1244.21793
## model_nameSantamo -18693.05593 -10490.41693
## model_nameSantana -20723.33629 -11688.01371
## model_nameSaxo -18760.24747 -11395.84616
## model_nameSC -11997.69402 532.04402
## model_nameSC7 -15211.34641 -7693.50359
## model_nameScenic -15363.04508 -9690.60752
## model_nameScirocco -12749.71867 -5048.48688
## model_nameScorpio -18569.65830 -12626.28170
## model_nameScudo -14966.62466 -8637.27845
## model_nameSebring -16070.62400 -9859.68600
## model_nameSedona -17672.19402 -5142.45598
## model_nameSeicento -21372.19402 -8842.45598
## model_nameSens -18780.18629 -9744.86371
## model_nameSentra -11646.18800 -4966.40937
## model_nameSephia -19030.10134 -12784.24563
## model_nameSequoia 5962.47121 22772.87879
## model_nameSerena -18630.62805 -11606.30767
## model_nameSeville -22537.52879 -5727.12121
## model_nameSharan -12249.56665 -6515.00442
## model_nameShuma -18442.54938 -12004.65342
## model_nameShuttle -17928.71196 -9369.27138
## model_nameSiena -24337.52879 -7527.12121
## model_nameSienna -5758.63705 784.80523
## model_nameSierra -18924.10078 -13071.46808
## model_nameSigma -23487.52879 -6677.12121
## model_nameSignum -14324.33949 -8210.45336
## model_nameSilver 2862.47121 19672.87879
## model_nameSilverado -7272.19402 5257.54402
## model_nameSintra -17373.25831 -11178.39003
## model_nameSkyline -14921.42164 -4191.59503
## model_nameSL320 -20537.52879 -3727.12121
## model_nameSL350 7993.23503 18723.06164
## model_nameSL380 2477.17121 19287.57879
## model_nameSL500 -6497.90497 4231.92164
## model_nameSLC200 22962.47121 39772.87879
## model_nameSLK200 -13137.52879 3672.87879
## model_nameSLK350 -11538.52879 5271.87879
## model_nameSLX -20837.52879 -4027.12121
## model_nameSmily -22237.52879 -5427.12121
## model_nameSobol -16587.65402 -4057.91598
## model_nameSolano -17460.07167 -7754.57833
## model_nameSolara -15205.07878 -7002.43979
## model_nameSolaris -10620.10596 -4818.22278
## model_nameSonata -14129.94127 -8336.47028
## model_nameSorento -8587.00030 -2840.74467
## model_nameSoul -10854.47202 -3490.07071
## model_nameSpace Gear -19130.57164 -8400.74503
## model_nameSpace Runner -18977.65236 -12038.99764
## model_nameSpace Star -16825.61016 -10841.82160
## model_nameSpace Wagon -17710.51636 -11608.01550
## model_nameSpark -14762.00570 -7823.35097
## model_nameSpectra -16933.45107 -9008.94893
## model_nameSpider -6037.52879 10772.87879
## model_nameSplash -14282.44641 -6764.60359
## model_nameSportage -7163.35833 -1431.79329
## model_nameSprinter -8409.05304 -2706.83670
## model_nameSRX -11349.60756 -3648.37577
## model_nameStarex -15560.07167 -5854.57833
## model_nameStarlet -24187.52879 -7377.12121
## model_nameStavic -9137.52879 7672.87879
## model_nameStealth -17097.19402 -4567.45598
## model_nameStilo -16495.88214 -10267.97433
## model_nameStratus -17128.73841 -11213.73131
## model_nameStream -15793.43732 -7868.93518
## model_nameStreetwise -20937.52879 -4127.12121
## model_nameSTS -18537.52879 -1727.12121
## model_nameSuburban -17923.80879 -1113.40121
## model_nameSunbird -24687.52879 -7877.12121
## model_nameSunny -19006.44473 -12892.55860
## model_nameSuperb -6881.96255 -1113.65029
## model_nameSupra -20037.52879 -3227.12121
## model_nameSwift -16763.54569 -10220.10341
## model_nameSX4 -11334.12091 -4954.29058
## model_nameSX4 S-Cross -19537.52879 -2727.12121
## model_nameSymbol -17221.34881 -9987.30119
## model_nameT1 -16762.04453 -10888.37535
## model_nameT2 -14988.00918 -8702.96340
## model_nameT3 -17931.46134 -11685.60563
## model_nameT3 Caravelle -19434.04167 -9728.54833
## model_nameT3 Multivan -20347.19402 -7817.45598
## model_nameT4 -14179.06673 -8466.25125
## model_nameT4 Caravelle -12947.12161 -6987.52839
## model_nameT4 Multivan -11116.80995 -4991.00590
## model_nameT5 -8063.08769 -2124.79308
## model_nameT5 Caravelle -3358.39893 2625.38963
## model_nameT5 Multivan 426.04328 6380.06156
## model_nameT5 Shuttle -9630.57164 1099.25497
## model_nameT6 2902.76169 13632.58831
## model_nameT6 Caravelle 4102.45688 11803.68867
## model_nameT6 Multivan 14777.80598 27307.54402
## model_nameTacoma -1325.10879 15485.29879
## model_nameTacuma -18309.98629 -9274.66371
## model_nameTahoe -1329.98629 7705.33629
## model_nameTalisman -5449.87862 3109.56196
## model_nameTaunus -19035.07167 -9329.57833
## model_nameTaurus -17620.37862 -9060.93804
## model_nameTavria -19963.04941 -12445.20659
## model_nameTeana -10090.99298 -3653.09702
## model_nameTempo -24537.52879 -7727.12121
## model_nameTempra -19131.74466 -12802.39845
## model_nameTeramont -37.52879 16772.87879
## model_nameTerracan -12885.80057 -6532.06372
## model_nameTerrano -10662.77521 -4309.03836
## model_nameThalia -22537.52879 -5727.12121
## model_nameThema -24637.52879 -7827.12121
## model_nameThesis -16910.07167 -7204.57833
## model_nameThunderbird -21497.19402 -8967.45598
## model_nameTiburon -15882.68215 -8648.63452
## model_nameTiggo -12897.55578 -6459.65982
## model_nameTigra -17969.48519 -10945.16481
## model_nameTiguan -5463.72292 357.43728
## model_nameTiida -12114.13508 -5251.31492
## model_nameTipo -19519.07376 -12494.75338
## model_nameTitan -5097.19402 7432.54402
## model_nameTL -12738.60756 -5037.37577
## model_nameTLX -7037.52879 9772.87879
## model_nameToledo -18028.92400 -12173.48899
## model_nameTouareg -6333.99446 -542.12101
## model_nameTouran -10878.79328 -5164.35352
## model_nameTourneo -10958.65214 -3836.76709
## model_nameTourneo Custom 1624.92833 11330.42167
## model_nameTown Car -13659.39785 -6864.19333
## model_nameTown&Country -11373.29910 -5292.10779
## model_nameTracker -19597.57164 -8867.74503
## model_nameTrafic -12412.72275 -6217.85447
## model_nameTrail Blazer -10704.59838 -3340.19707
## model_nameTrajet -15905.81314 -9171.28130
## model_nameTrans Sport -18869.57607 -10945.07393
## model_nameTransit -14504.46225 -8826.77168
## model_nameTrax -10202.42425 -3467.89242
## model_nameTribeca -10959.05642 -4279.27779
## model_nameTribute -14420.42137 -7298.53632
## model_nameTSX -10426.69947 -3402.37910
## model_nameTT -10635.21657 -3270.81525
## model_nameTucson -7701.69369 -1724.25735
## model_nameTundra -1641.24641 5876.59641
## model_nameTwingo -17677.34647 -11269.53430
## model_nameUlysse -15602.69260 -9678.77064
## model_nameUno -20247.57167 -10542.07833
## model_nameUp -14767.57164 -4037.74503
## model_nameUrban Cruiser -13678.14831 -2948.32169
## model_nameUrvan -23537.52879 -6727.12121
## model_nameV220 5141.14971 14176.47229
## model_nameV250 20845.41371 29880.73629
## model_nameV40 -15090.36694 -8925.21043
## model_nameV50 -12792.71373 -5997.50921
## model_nameV60 -4517.60541 3000.23741
## model_nameV70 -12184.13705 -5640.69477
## model_nameV8 -19037.52879 -2227.12121
## model_nameV90 21902.80598 34432.54402
## model_nameVan -11652.19402 877.54402
## model_nameVaneo -16269.62805 -9245.30767
## model_nameVanette -18307.57978 -11869.68382
## model_nameVario -11605.27423 -3904.04244
## model_nameVectra -16571.83665 -10928.83632
## model_nameVel Satis -15250.14763 -8842.33545
## model_nameVenga -13892.81164 -5690.17264
## model_nameVento -18051.17140 -12097.15312
## model_nameVenture -20447.19402 -7917.45598
## model_nameVenza -1877.85236 5060.80236
## model_nameVerona -18114.23831 -7384.41169
## model_nameVersa -17247.19402 -4717.45598
## model_nameVerso -8795.82535 -2466.47914
## model_nameVesta -8833.98076 -2879.96247
## model_nameViano -5347.46239 1032.36794
## model_nameVibe -14414.63956 -7909.24087
## model_nameVIS -23093.10879 -6282.70121
## model_nameVision -24087.52879 -7277.12121
## model_nameVitara -12249.89450 -4047.25550
## model_nameVito -10741.67391 -4952.91625
## model_nameVivaro -9257.97692 -2972.93114
## model_nameVolt -5069.50164 3133.13736
## model_nameVoyager -15762.52480 -9939.22880
## model_nameWagon R -18623.65215 -11389.60452
## model_nameWindstar -18909.98629 -9874.66371
## model_nameWrangler -2309.21629 6726.10629
## model_nameWRX -137.52879 16672.87879
## model_nameX-Trail -6752.14232 -904.93124
## model_nameX-Type -15249.34881 -8015.30119
## model_nameX1 -3808.51844 2283.09481
## model_nameX2 11006.72121 27817.12879
## model_nameX3 -3926.38700 1953.96156
## model_nameX4 12397.92393 20322.42607
## model_nameX5 -4226.79718 1451.63815
## model_nameX5 M 6099.23550 14301.87450
## model_nameX50 -12895.54529 -4336.10471
## model_nameX6 5366.74029 11227.97348
## model_nameX6 M 2059.87804 10619.31862
## model_nameX60 -11237.17341 -4693.73113
## model_nameX70 -6820.71196 1738.72862
## model_nameXantia -18051.59442 -12385.16292
## model_nameXC60 -822.04105 5221.56819
## model_nameXC70 -7639.30021 -1285.56336
## model_nameXC90 -6693.90904 -905.15138
## model_nameXE 5993.45471 14552.89529
## model_nameXedos 6 -18476.29402 -12191.24824
## model_nameXedos 9 -17650.18570 -10711.53097
## model_nameXF -3531.72907 3590.15599
## model_nameXG 30 -22337.52879 -5527.12121
## model_nameXJ 360.27093 7482.15599
## model_nameXKR -8037.52879 8772.87879
## model_nameXL7 -14610.32167 -4904.82833
## model_nameXM -18778.89550 -11983.69098
## model_nameXRAY -9819.23491 -3024.03039
## model_nameXsara -17269.66523 -11510.51449
## model_nameXsara Picasso -15819.67817 -9952.37352
## model_nameXV -5528.84978 2172.38201
## model_nameYaris -14381.94815 -8591.64558
## model_nameYeti -9915.96920 -3651.10018
## model_nameYpsilon -20057.73831 -9327.91169
## model_nameZ3 -17787.52879 -977.12121
## model_nameZ4 -15237.52879 1572.87879
## model_nameZafira -13101.17369 -7435.09395
## model_nameZDX -2457.89732 5466.60482
## model_nameZeta -16765.88241 -9248.03959
## model_nameZX -19035.93301 -12628.12083sigma(modelPrice)*100/mean(cars_edited$price_usd)## [1] 60.95455Our prediction error rate is lower than for other attributes (60.95455%) which confirms to us that model name is a better predictor of price. Nevertheless, a prediction error of 60.95455% tells us that for prediction we will need more attributes.
This analysis concludes that the popularity of a vehicle does seem to have an impact on the average_price of a vehicle. However, more attributes would be needed to predict price.
Q8: What is the average age of each vehicle manufacturer and whether the manufacturer changes how the production year impacts the price?
A: The average age of each manufacturer can be found using some data transformation. Furthermore, the manufacturer does influence how production year changes the price of a vehicle.
Justification:
Prior to working on this question it would be useful to have some sort of visual understanding of our data. To accomplish this we will use a scatter plat with facet wrap to show the distribution of years for each manufacturer.
#Scatter plot: Year produced by price and colored by manufacturer name
ggplot(cars_edited, aes(x = year_produced, y = price_usd)) + geom_hex() + facet_wrap(~ manufacturer_name)
Upon looking at the graph it is clear that visualization is not the way to solve this question. Nevertheless, the graphs seem to follow a similar pattern; more newer cars exist and they are more expensive.
To solve for the average age of each manufacturer we will perform a data transformation as follows:
#Group cars by manufacturer name
manufacturer_year <- group_by(cars_edited, manufacturer_name)
#Summarize the manufacturer years average
manufacturer_year_averages <- summarise(manufacturer_year, average = mean(year_produced, na.rm = TRUE))
# 1) Average age of each vehicle manufacturer
manufacturer_year_averages %>% arrange(desc(average))## # A tibble: 55 x 2
## manufacturer_name average
## <chr> <dbl>
## 1 Lifan 2015.
## 2 Buick 2014.
## 3 Geely 2014.
## 4 LADA 2014.
## 5 Skoda 2013.
## 6 Mini 2011.
## 7 Chevrolet 2011.
## 8 Chery 2011.
## 9 Great Wall 2009.
## 10 Dacia 2009.
## # ... with 45 more rowsObserving the results of the summary it is clear that the manufacturers with the newest car averages are Lifan, Buick, Geely, and LADA.
Next we continue with the second part of the question: Whether the manufacturer changes how the production year impacts the price?
To solve this question we will be using the Two-Way Anova. The Two-Way Anova is used because we are attempting to evaluate the simultaneous effect of Manufacturer and Year Produced on price.
The hypotheses for the Two-Way Anova test are:
Ho = 1. There is no difference in the means of Manufacturer. There is no difference in the means of Year Produced. 3. There is no interaction between Manufacturer and Year Produced
Ha = For cases 1 and 2 the means are not equal and for case 3 there is an interaction between Manufacturer and Year Produced.
For this test we will be using 0.05 as our level of significance. The results are thus:
# Do an anova test to see if year produced significantly impacts price of a vehicle
manufacturer_price <- aov(price_usd ~ manufacturer_name * year_produced, data = cars_edited)
summary(manufacturer_price)## Df Sum Sq Mean Sq F value Pr(>F)
## manufacturer_name 54 2.917e+11 5.402e+09 428.4 <2e-16 ***
## year_produced 1 6.854e+11 6.854e+11 54355.5 <2e-16 ***
## manufacturer_name:year_produced 54 1.273e+11 2.357e+09 186.9 <2e-16 ***
## Residuals 38380 4.840e+11 1.261e+07
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1In our results we can see that the p-value < 0.05 for each of our 3 cases. In other words, there is a difference in the means of Manufacturer, the means of Year Produced, and there is an interaction between Manufacturer and Year Produced. The manufacturer does change how the production year affects the selling price.
Now that we know that Manufacturer and Year Produced impact the price of our vehicle it important to see how well of a model we can produce given these two attributes. To do this we will use the Multiple Linear Regression Model.
#Taking the linear regression
ManufyearPrice <- lm (price_usd ~ manufacturer_name + year_produced, data = cars_edited)
#Making sure the linear regression line matches the model
summary(ManufyearPrice)##
## Call:
## lm(formula = price_usd ~ manufacturer_name + year_produced, data = cars_edited)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14133 -2147 -519 1134 44499
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.140e+06 5.573e+03 -204.505 < 2e-16 ***
## manufacturer_nameAlfa Romeo -5.591e+03 5.642e+02 -9.910 < 2e-16 ***
## manufacturer_nameAudi -1.586e+03 4.978e+02 -3.187 0.00144 **
## manufacturer_nameAvtoVAZ -3.893e+03 5.247e+02 -7.421 1.19e-13 ***
## manufacturer_nameBMW -6.043e+02 4.972e+02 -1.215 0.22420
## manufacturer_nameBuick -4.103e+03 7.614e+02 -5.388 7.16e-08 ***
## manufacturer_nameCadillac -1.036e+03 7.816e+02 -1.326 0.18499
## manufacturer_nameChery -1.024e+04 7.178e+02 -14.260 < 2e-16 ***
## manufacturer_nameChevrolet -5.946e+03 5.268e+02 -11.287 < 2e-16 ***
## manufacturer_nameChrysler -4.698e+03 5.291e+02 -8.879 < 2e-16 ***
## manufacturer_nameCitroen -6.049e+03 5.013e+02 -12.068 < 2e-16 ***
## manufacturer_nameDacia -8.635e+03 7.145e+02 -12.085 < 2e-16 ***
## manufacturer_nameDaewoo -7.848e+03 5.596e+02 -14.023 < 2e-16 ***
## manufacturer_nameDodge -5.002e+03 5.428e+02 -9.215 < 2e-16 ***
## manufacturer_nameFiat -5.770e+03 5.105e+02 -11.301 < 2e-16 ***
## manufacturer_nameFord -4.785e+03 4.974e+02 -9.621 < 2e-16 ***
## manufacturer_nameGAZ 2.130e+03 5.686e+02 3.746 0.00018 ***
## manufacturer_nameGeely -8.969e+03 6.822e+02 -13.149 < 2e-16 ***
## manufacturer_nameGreat Wall -7.699e+03 8.263e+02 -9.317 < 2e-16 ***
## manufacturer_nameHonda -4.274e+03 5.109e+02 -8.366 < 2e-16 ***
## manufacturer_nameHyundai -4.627e+03 5.052e+02 -9.159 < 2e-16 ***
## manufacturer_nameInfiniti 5.880e+02 5.824e+02 1.010 0.31262
## manufacturer_nameIveco -2.139e+02 5.963e+02 -0.359 0.71978
## manufacturer_nameJaguar 4.243e+03 7.356e+02 5.768 8.06e-09 ***
## manufacturer_nameJeep -4.869e+02 6.242e+02 -0.780 0.43541
## manufacturer_nameKia -4.973e+03 5.083e+02 -9.782 < 2e-16 ***
## manufacturer_nameLADA -9.049e+03 5.918e+02 -15.290 < 2e-16 ***
## manufacturer_nameLancia -5.546e+03 6.436e+02 -8.616 < 2e-16 ***
## manufacturer_nameLand Rover 2.458e+03 5.722e+02 4.295 1.75e-05 ***
## manufacturer_nameLexus 3.756e+03 5.618e+02 6.686 2.33e-11 ***
## manufacturer_nameLifan -9.028e+03 7.615e+02 -11.856 < 2e-16 ***
## manufacturer_nameLincoln -6.532e+02 8.264e+02 -0.791 0.42924
## manufacturer_nameMazda -4.846e+03 5.032e+02 -9.631 < 2e-16 ***
## manufacturer_nameMercedes-Benz -2.389e+02 4.983e+02 -0.479 0.63163
## manufacturer_nameMini -1.706e+03 6.892e+02 -2.475 0.01332 *
## manufacturer_nameMitsubishi -4.406e+03 5.090e+02 -8.655 < 2e-16 ***
## manufacturer_nameMoskvitch 4.373e+03 7.323e+02 5.972 2.37e-09 ***
## manufacturer_nameNissan -4.452e+03 5.027e+02 -8.856 < 2e-16 ***
## manufacturer_nameOpel -5.383e+03 4.969e+02 -10.832 < 2e-16 ***
## manufacturer_namePeugeot -5.964e+03 4.994e+02 -11.942 < 2e-16 ***
## manufacturer_namePontiac -4.690e+03 7.874e+02 -5.956 2.60e-09 ***
## manufacturer_namePorsche 5.437e+03 7.083e+02 7.676 1.68e-14 ***
## manufacturer_nameRenault -5.894e+03 4.975e+02 -11.848 < 2e-16 ***
## manufacturer_nameRover -5.704e+03 5.562e+02 -10.256 < 2e-16 ***
## manufacturer_nameSaab -4.979e+03 6.233e+02 -7.988 1.41e-15 ***
## manufacturer_nameSeat -5.163e+03 5.420e+02 -9.525 < 2e-16 ***
## manufacturer_nameSkoda -2.145e+03 5.062e+02 -4.237 2.27e-05 ***
## manufacturer_nameSsangYong -4.715e+03 6.650e+02 -7.090 1.37e-12 ***
## manufacturer_nameSubaru -3.727e+03 5.438e+02 -6.854 7.28e-12 ***
## manufacturer_nameSuzuki -5.732e+03 5.559e+02 -10.312 < 2e-16 ***
## manufacturer_nameToyota -2.302e+03 5.037e+02 -4.570 4.90e-06 ***
## manufacturer_nameUAZ -4.713e+03 6.756e+02 -6.977 3.06e-12 ***
## manufacturer_nameVolkswagen -3.251e+03 4.949e+02 -6.570 5.10e-11 ***
## manufacturer_nameVolvo -2.688e+03 5.129e+02 -5.240 1.61e-07 ***
## manufacturer_nameZAZ -5.519e+03 7.877e+02 -7.007 2.47e-12 ***
## year_produced 5.742e+02 2.766e+00 207.604 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3988 on 38434 degrees of freedom
## Multiple R-squared: 0.6152, Adjusted R-squared: 0.6146
## F-statistic: 1117 on 55 and 38434 DF, p-value: < 2.2e-16The R2 is 0.6152 which indicates that a relatively large proportion of prices in the data can be explained by the model. Although more attributes will aid in predicting prices we can confirm that there is significant usefulness in using Manufacturer and Year Produced in our future models.
To continue our investigation we assess the prediction rate as follows:
confint(ManufyearPrice)## 2.5 % 97.5 %
## (Intercept) -1150550.3835 -1128705.4004
## manufacturer_nameAlfa Romeo -6696.4389 -4484.9382
## manufacturer_nameAudi -2562.1524 -610.8145
## manufacturer_nameAvtoVAZ -4921.8093 -2865.0381
## manufacturer_nameBMW -1578.8908 370.2152
## manufacturer_nameBuick -5595.0018 -2610.2026
## manufacturer_nameCadillac -2567.8861 495.8650
## manufacturer_nameChery -11643.0998 -8829.2082
## manufacturer_nameChevrolet -6978.9568 -4913.7873
## manufacturer_nameChrysler -5735.4878 -3661.2753
## manufacturer_nameCitroen -7031.8311 -5066.8047
## manufacturer_nameDacia -10035.8648 -7234.8567
## manufacturer_nameDaewoo -8944.9157 -6751.1196
## manufacturer_nameDodge -6065.5282 -3937.7509
## manufacturer_nameFiat -6770.2861 -4768.9920
## manufacturer_nameFord -5759.9662 -3810.2375
## manufacturer_nameGAZ 1015.3929 3244.3169
## manufacturer_nameGeely -10306.3586 -7632.2903
## manufacturer_nameGreat Wall -9318.5557 -6079.3889
## manufacturer_nameHonda -5275.4681 -3272.7342
## manufacturer_nameHyundai -5617.0639 -3636.6834
## manufacturer_nameInfiniti -553.4006 1729.4829
## manufacturer_nameIveco -1382.5997 954.7819
## manufacturer_nameJaguar 2801.3329 5684.7834
## manufacturer_nameJeep -1710.3565 736.6019
## manufacturer_nameKia -5969.1345 -3976.4180
## manufacturer_nameLADA -10208.9582 -7889.0083
## manufacturer_nameLancia -6807.0860 -4283.9923
## manufacturer_nameLand Rover 1336.1772 3579.1920
## manufacturer_nameLexus 2654.8911 4857.2551
## manufacturer_nameLifan -10520.7052 -7535.7338
## manufacturer_nameLincoln -2272.9355 966.4434
## manufacturer_nameMazda -5832.1261 -3859.6730
## manufacturer_nameMercedes-Benz -1215.5909 737.7845
## manufacturer_nameMini -3056.5676 -355.0096
## manufacturer_nameMitsubishi -5403.6473 -3408.1937
## manufacturer_nameMoskvitch 2937.6360 5808.1012
## manufacturer_nameNissan -5437.3948 -3466.6670
## manufacturer_nameOpel -6356.7827 -4408.8060
## manufacturer_namePeugeot -6943.2019 -4985.3917
## manufacturer_namePontiac -6233.0210 -3146.4893
## manufacturer_namePorsche 4048.7994 6825.3766
## manufacturer_nameRenault -6868.8037 -4918.7037
## manufacturer_nameRover -6793.9860 -4613.8458
## manufacturer_nameSaab -6200.6518 -3757.1769
## manufacturer_nameSeat -6225.3147 -4100.5875
## manufacturer_nameSkoda -3136.7430 -1152.4208
## manufacturer_nameSsangYong -6018.5656 -3411.5550
## manufacturer_nameSubaru -4792.8442 -2661.2810
## manufacturer_nameSuzuki -6821.7444 -4642.6653
## manufacturer_nameToyota -3289.2724 -1314.6324
## manufacturer_nameUAZ -6037.5839 -3389.3607
## manufacturer_nameVolkswagen -4221.5412 -2281.4538
## manufacturer_nameVolvo -3693.2558 -1682.5705
## manufacturer_nameZAZ -7063.3664 -3975.6282
## year_produced 568.7353 579.5768sigma(ManufyearPrice)*100/mean(cars_edited$price_usd)## [1] 60.12396Our prediction error rate is lower than for other attributes (60.12396%) which confirms to us that year produced and Manufacturer are decent predictors of price. A prediction error of 60.12396% tells us that for prediction we will need more attributes than just year produced and Manufacturer. We conclude that Manufacturer and Year Produced do impact price and that the manufacturer does change how the production year affects the selling price.
Model Results and Comparisons
In an attempt to create the most accurate and significant predictive model we created 6 different models which had a wide range of accuracy.
Linear Regression Model
Firstly, we created a Multiple Linear Regression Model which utilized the attributes in our dataset that had continuous data. The code for this looks as follows:
LMCont <- lm(price_usd ~ odometer_value
+ year_produced
+ number_of_photos
+ duration_listed
+ up_counter
, data = train.data)
step.LMConts <- LMCont %>% stepAIC(trace = FALSE)Now that the model is created it is vital to check the accuracy of the model. To do this we will employed our test.data to test how accurately the Linear Regression model can predict the price of a vehicle.
# Predict using Multiple Linear Regression Model
LMContPrediction <- predict(step.LMConts, test.data)
# Prediction error, rmse
RMSE(LMContPrediction,test.data$price_usd) # RMSE is 4573.511## [1] 4573.511# Compute R-square
R2(LMContPrediction,test.data$price_usd) ## R^2 for test/train is 50.95891%## [1] 0.5095891From the code above we see that our rmse is 4573.511 which represents an error rate of 4573.511/mean(test.data$price_usd) = 68.60393 which is not good at all. Meanwhile, the R2 is 0.5095891, meaning that the observed and predicted outcome values are not very correlated, which is not good. These results are not surprising whatsoever and merely inform us that the price of a vehicle in Belarus is dependent on more attributes than simply our continuous attributes. We shall proceed with more robust models in order to achieve a better result.
After doing the Linear Regression Model we attempted to use SVR in order to create a more robust predictive model.
SVR Model
SVR is an extremely robust model which would be able to handle our categorical data and almost certainly achieve a better result that the Linear Regression Model. 3 SVR models were calculated were varying accuracies. The different methods used with SVR were linear, polynomial, and radial. Linear was the first SVR model to be run and the code was as follows:
# Create SVR Model using Linear Method
modelSVRLinTrain <- train( price_usd ~ ., data = train.data, method = "svmLinear",
trControl = trainControl("cv", number =10),
preProcess = c("center", "scale"),
tuneLength = 10
)
summary(modelSVRLinTrain)
#Length Class Mode
#1 ksvm S4
modelSVRLinTrain$bestTune
#C
#1 1
# Predict using SVR Model with Linear Method
modelSVRLinTrainPrediction <- predict(modelSVRLinTrain, test.data)
# Prediction error, rmse
RMSE(modelSVRLinTrainPrediction,test.data$price_usd)
#[1] 3257.887
# Compute R-square
R2(modelSVRLinTrainPrediction,test.data$price_usd)
#[1] 0.7772176Afterwords, the polynomial method was used with the SVR model:
# Create SVR Model using Polynomial Method
Method
modelSVRPolyTrain <- train(price_usd ~ ., data = train.data, method = "svmPoly",
trControl = trainControl("cv", number =10),
preProcess = c("center", "scale"),
tuneLength = 10
)
summary(modelSVRRadialTrain)
#Length Class Mode
#1 ksvm S4
modelSVRRadialTrain$bestTune
#sigma C
#10 0.001556481 128
# Predict using SVR Model with Linear Method
modelSVRPolyTrainPrediction <- predict(modelSVRPolyTrain, test.data)
# Prediction error, rmse
RMSE(modelSVRPolyTrainPrediction,test.data$price_usd)
# Compute R-square
R2(modelSVRPolyTrainPrediction,test.data$price_usd)Lastly, the radial method was used with the SVR model:
# Create SVR Model using Radial Method
modelSVRRadialTrain <- train(price_usd ~ ., data = train.data, method = "svmRadial",
trControl = trainControl("cv", number =10),
preProcess = c("center", "scale"),
tuneLength = 10
)
summary(modelSVRRadialTrain)
modelSVRRadialTrain$bestTune
# Predict using SVR Model with Radial Method
modelSVRRadialTrainPrediction <- predict(modelSVRRadialTrain, test.data)
# Prediction error, rmse
RMSE(modelSVRRadialTrainPrediction,test.data$price_usd)
#[1] 4752.231
# Compute R-square
R2(modelSVRRadialTrainPrediction,test.data$price_usd)
#[1] 0.5937837Even though the SVR model did much better than the Linear Regression Model it was vital to investigate more models in an attempt to create an even more accurate model. The Decision Tree was a price candidate since it is incredibly robust and relies on very few assumptions. Its simpler nature also makes it a model that would be preferred over other models(such as Random Forest Regression or KNN).
Decision Tree Regression Model
model_DT_Train <- train(price_usd ~ ., data = train.data, method = "rpart",
trControl = trainControl("cv",number = 10),
preProcess = c("center","scale"),
tuneLength = 10)
summary(model_DT_Train)# Plot the final tree model
par(xpd = NA) # Avoid clipping the text in some device
plot(model_DT_Train$finalModel)
text(model_DT_Train$finalModel, digits = 3)
#Decision rules in the model model_DT_Train$finalModel
# Make predictions on the test data prediction_DT_Train <- model_DT_Train %>% predict(test.data)
# Prediction error, rmse RMSE(prediction_DT_Train,test.data$price_usd)
#[1] 3245.413
# Compute R-square R2(prediction_DT_Train,test.data$price_usd)
#[1] 0.7529956Random Forest Tree Model
random_forest_ranger <- train(price_usd ~ . ,
data = train.data,
method = "ranger",
trControl = trainControl("cv", number = 10),
preProcess = c("center","scale"),
tuneLength = 10
)
summary(random_forest_ranger)
random_forest_ranger$bestTune
plot(random_forest_ranger)
# Plot the final tree model
par(xpd = NA) # Avoid clipping the text in some device
plot(random_forest_ranger$finalModel)
text(random_forest_ranger$finalModel, digits = 3)
# Make predictions on the test data
rf_predict_ranger <- predict(random_forest_ranger, test.data , type='response')
# Prediction error, rmse
RMSE(rf_predict_ranger,test.data$price_usd)
# Compute R-square
R2(rf_predict_ranger,test.data$price_usd)KNN Model
model_knn <- train(
price_usd ~., data = train.data, method = "knn",
trControl = trainControl("cv", number = 10),
preProcess = c("center","scale"),
tuneLength = 20
)
summary(model_knn$finalModel)# Print the best tuning parameter k that maximizes model accuracy
model_knn$bestTune
#k
#1 5# Plot model accuracy vs different values of k
plot(model_knn)
# Make predictions on the test data
knn_predictions <- model_knn %>% predict(test.data)
head(knn_predictions)
#[1] 9560.000 9000.000 4580.000 5648.494 8575.200 7720.000# Compute the prediction error RMSE
RMSE(knn_predictions,test.data$price_usd)
#[1] 3693.127
# Compute R-square
R2(knn_predictions,test.data$price_usd)
#[1] 0.6802895
Compare results obtained using different ML methods
Our choices: Linear Regression Decision Trees SVR (Linear and NonLinear) Random Forest Regression KNN